An accurate record of building footprints is important for a range of applications, from population estimation and urban planning to humanitarian response and environmental science. After a disaster, such as a flood or an earthquake, authorities need to estimate how many households have been affected. Ideally there would be up-to-date census information for this, but in practice such records may be out of date or unavailable. Instead, data on the locations and density of buildings can be a valuable alternative source of information.
A good way to collect such data is through satellite imagery, which can map the distribution of buildings across the world, particularly in areas that are isolated or difficult to access. However, detecting buildings with computer vision methods in some environments can be a challenging task. Because satellite imaging involves photographing the earth from several hundred kilometres above the ground, even at high resolution (30–50 cm per pixel), a small building or tent shelter occupies only a few pixels. The task is even more difficult for informal settlements, or rural areas where buildings constructed with natural materials can visually blend into the surroundings. There are also many types of natural and artificial features that can be easily confused with buildings in overhead imagery.
In “Continental-Scale Building Detection from High-Resolution Satellite Imagery”, we address these challenges, using new methods for detecting buildings that work in rural and urban settings across different terrains, such as savannah, desert, and forest, as well as informal settlements and refugee facilities. We use this building detection model to create the Open Buildings dataset, a new open-access data resource containing the locations and footprints of 516 million buildings with coverage across most of the African continent. The dataset will support several practical, scientific and humanitarian applications, ranging from disaster response or population mapping to planning services such as new medical facilities or studying human impact on the natural environment.
Model Development We built a training dataset for the building detection model by manually labelling 1.75 million buildings in 100k images. The figure below shows some examples of how we labelled images in the training data, taking into account confounding characteristics of different areas across the African continent. In rural areas, for example, it was necessary to identify different types of dwelling places and to disambiguate them from natural features, while in urban areas we needed to develop labelling policies for dense and contiguous structures.
We trained the model to detect buildings in a bottom-up way, first by classifying each pixel as building or non-building, and then grouping these pixels together into individual instances. The detection pipeline was based on the U-Net model, which is commonly used in satellite image analysis. One advantage of U-Net is that it is a relatively compact architecture, and so can be applied to large quantities of imaging data without a heavy compute burden. This is critical, because the final task of applying this to continental-scale satellite imagery means running the model on many billions of image tiles.
Initial experiments with the basic model had low precision and recall, for example due to the variety of natural and artificial features with building-like appearance. We found a number of methods that improved performance. One was the use of mixup as a regularisation method, where random training images are blended together by taking a weighted average. Though mixup was originally proposed for image classification, we modified it to be used for semantic segmentation. Regularisation is important in general for this building segmentation task, because even with 100k training images, the training data do not capture the full variation of terrain, atmospheric and lighting conditions that the model is presented with at test time, and hence, there is a tendency to overfit. This is mitigated by mixup as well as random augmentation of training images.
Another method that we found to be effective was the use of unsupervised self-training. We prepared a set of 100 million satellite images from across Africa, and filtered these to a subset of 8.7 million images that mostly contained buildings. This dataset was used for self-training using the Noisy Student method, in which the output of the best building detection model from the previous stage is used as a ‘teacher’ to then train a ‘student’ model that makes similar predictions from augmented images. In practice, we found that this reduced false positives and sharpened the detection output. The student model gave higher confidence to buildings and lower confidence to background.
One problem that we faced initially was that our model had a tendency to create “blobby” detections, without clearly delineated edges and with a tendency for neighbouring buildings to be merged together. To address this, we applied another idea from the original U-Net paper, which is to use distance weighting to adapt the loss function to emphasise the importance of making correct predictions near boundaries. During training, distance weighting places greater emphasis at the edges by adding weight to the loss — particularly where there are instances that nearly touch. For building detection, this encourages the model to correctly identify the gaps in between buildings, which is important so that many close structures are not merged together. We found that the original U-Net distance weighting formulation was helpful but slow to compute. So, we developed an alternative based on Gaussian convolution of edges, which was both faster and more effective.
Our technical report has more details on each of these methods.
Results We evaluated the performance of the model on several different regions across the continent, in different categories: urban, rural, and medium-density. In addition, with the goal of preparing for potential humanitarian applications, we tested the model on regions with displaced persons and refugee settlements. Precision and recall did vary between regions, so achieving consistent performance across the continent is an ongoing challenge.
When visually inspecting the detections for low-scoring regions, we noted various causes. In rural areas, label errors were problematic. For example, single buildings within a mostly-empty area can be difficult for labellers to spot. In urban areas, the model had a tendency to split large buildings into separate instances. The model also underperformed in desert terrain, where buildings were hard to distinguish against the background.
We carried out an ablation study to understand which methods contributed most to the final performance, measured in mean average precision (mAP). Distance weighting, mixup and the use of ImageNet pre-training were the biggest factors for the performance of the supervised learning baseline. The ablated models that did not use these methods had a mAP difference of -0.33, -0.12 and -0.07 respectively. Unsupervised self-training gave a further significant boost of +0.06 mAP.
Generating the Open Buildings Dataset To create the final dataset, we applied our best building detection model to satellite imagery across the African continent (8.6 billion image tiles covering 19.4 million km2, 64% of the continent), which resulted in the detection of 516M distinct structures.
Each building’s outline was simplified as a polygon and associated with a Plus Code, which is a geographic identifier made up of numbers and letters, akin to a street address, and useful for identifying buildings in areas that don’t have formal addressing systems. We also include confidence scores and guidance on suggested thresholds to achieve particular precision levels.
The sizes of the structures vary as shown below, tending towards small footprints. The inclusion of small structures is important, for example, to support analyses of informal settlements or refugee facilities.
The data is freely available and we look forward to hearing how it is used. In the future, we may add new features and regions, depending on usage and feedback.
Acknowledgements This work is part of our AI for Social Good efforts and was led by Google Research, Ghana. Thanks to the co-authors of this work: Wojciech Sirko, Sergii Kashubin, Marvin Ritter, Abigail Annkah, Yasser Salah Edine Bouchareb, Yann Dauphin, Daniel Keysers, Maxim Neumann and Moustapha Cisse. We are grateful to Abdoulaye Diack, Sean Askay, Ruth Alcantara and Francisco Moneo for help with coordination. Rob Litzke, Brian Shucker, Yan Mayster and Michelina Pallone provided valuable assistance with geo infrastructure.
In December 2018, we introduced TF-Ranking, an open-source TensorFlow-based library for developing scalable neural learning-to-rank (LTR) models, which are useful in settings where users expect to receive an ordered list of items in response to their query. LTR models — unlike standard classification models that classify one item at a time — receive an entire list of items as an input, and learn an ordering that maximizes the utility of the entire list. While search and recommendation systems are the most common applications of LTR models, since its release, we have seen TF-Ranking being applied in diverse domains beyond search, including e-commerce, SAT solvers, and smart city planning.
In May 2021, we published a major release of TF-Ranking that enables full support for natively building LTR models using Keras, a high-level API of TensorFlow 2. Our native Keras ranking model has a brand-new workflow design, including a flexible ModelBuilder, a DatasetBuilder to set up training data, and a Pipeline to train the model with the provided dataset. These components make building a customized LTR model easier than ever, and facilitate rapid exploration of new model structures for production and research. If RaggedTensors are your tool of choice, TF-Ranking is now working with them as well. In addition, our most recent release, which incorporates the Orbit training library, contains a long list of advances — the culmination of two and half years of neural LTR research. Below we share a few of the key improvements available in the latest TF-Ranking version.
Learning-to-Rank with TFR-BERT Recently, pretrained language models like BERT have achieved state-of-the-art performance on various language understanding tasks. To capture the expressiveness of these models, TF-Ranking implements a novel TFR-BERT architecture that couples BERT with the power of LTR to optimize the ordering of list inputs. As an example, consider a query and a list of n documents that one might like to rank in response to this query. Instead of learning an independent BERT representation for each <query, document> pair, LTR models apply a ranking loss to jointly learn a BERT representation that maximizes the utility of the entire ranked list with respect to the ground-truth labels.
The figure below illustrates this process. First, we flatten a list of n documents to rank in response to a query into a list <query, document> tuples. These tuples are fed into a pre-trained language model (e.g., BERT). The pooled BERT outputs for the entire document list are then jointly fine-tuned with one of the specialized ranking losses available in TF-Ranking. Our experience shows that this TFR-BERT architecture delivers significant improvements in pretrained language model performance, leading to state-of-the-art performance for several popular ranking tasks, especially when multiple pretrained language models are ensembled. Our users can now get started with TFR-BERT using this simple example.
Interpretable Learning-to-Rank Transparency and interpretability are important factors in deploying LTR models in ranking systems that can be involved in determining the outcomes of processes such as loan eligibility assessment, advertisement targeting, or guiding medical treatment decisions. In such cases, the contribution of each individual feature to the final ranking should be examinable and understandable to ensure transparency, accountability and fairness of the outcomes.
One possible way to achieve this is using generalized additive models (GAMs) — intrinsically interpretable machine learning models that are linearly composed of smooth functions of individual features. However, while GAMs have been extensively studied on regression and classification tasks, it is less clear how to apply them in a ranking setting. For instance, while GAMs can be straightforwardly applied to model each individual item in the list, modeling both item interactions and the context in which these items are ranked is a more challenging research problem. To this end, we have developed a neural ranking GAM — an extension of generalized additive models to ranking problems.
Unlike standard GAMs, a neural ranking GAM can take into account both the features of the ranked items and the context features (e.g., query or user profile) to derive an interpretable, compact model. This ensures that not only the contribution of each item-level feature is interpretable, but also the contribution of the context features. For example, in the figure below, using a neural ranking GAM makes visible how distance, price, and relevance, in the context of a given user device, contribute to the final ranking of the hotel. Neural ranking GAMs are now available as a part of TF-Ranking,
Neural Ranking or Gradient Boosting? While neural models have achieved state of the art performance in multiple domains, specialized gradient boosted decision trees (GBDTs) like LambdaMART remained the baseline to beat in a variety of open LTR datasets. The success of GBDTs in open datasets is due to several reasons. First, due to their relatively small size, neural models are prone to overfitting on these datasets. Second, since GBDTs partition their input feature space using decision trees, they are naturally more resilient to variations in numerical scales in ranking data, which often contain features with Zipfian or otherwise skewed distributions. However, GBDTs do have their limitations in more realistic ranking scenarios, which often combine both textual and numerical features. For instance, GBDTs cannot be directly applied to large discrete feature spaces, such as raw document text. They are also, in general, less scalable than neural ranking models.
Therefore, since the TF-Ranking release, our team has significantly deepened the understanding of how best to leverage neural models in ranking with numerical features. This culminated in a Data Augmented Self-Attentive Latent Cross (DASALC) model, described in an ICLR 2021 paper, which is the first to establish parity, and in some cases statistically significant improvements, of neural ranking models over strong LambdaMART baselines on open LTR datasets. This achievement is made possible through a combination of techniques, which include data augmentation, neural feature transformation, self-attention for modeling document interactions, listwise ranking loss, and model ensembling similar to boosting in GBDTs. The architecture of the DASALC model was entirely implemented using the TF-Ranking library.
Conclusion All in all, we believe that the new Keras-based TF-Ranking version will make it easier to conduct neural LTR research and deploy production-grade ranking systems. We encourage everyone to try out the latest version and follow this introductory example for a hands-on experience. While we are very excited about this new release, our research and development journey is far from over, so we will continue to advance our understanding of learning-to-rank problems and share these advances with our users.
Acknowledgements This project was only possible thanks to the current and past members of the TF-Ranking team: Honglei Zhuang, Le Yan, Rama Pasumarthi, Rolf Jagerman, Zhen Qin, Shuguang Han, Sebastian Bruch, Nathan Cordeiro, Marc Najork and Patrick McGregor. We also extend special thanks to our collaborators from the Tensorflow team: Zhenyu Tan, Goldie Gadde, Rick Chao, Yuefeng Zhou, Hongkun Yu, and Jing Li.
For the ~466 million people in the world who are deaf or hard of hearing, the lack of easy access to accessibility services can be a barrier to participating in spoken conversations encountered daily. While hearing aids can help alleviate this, simply amplifying sound is insufficient for many. One additional option that may be available is the cochlear implant (CI), which is an electronic device that is surgically inserted into a part of the inner ear, called the cochlea, and stimulates the auditory nerve electrically via external sound processors. While many individuals with these cochlear implants can learn to interpret these electrical stimulations as audible speech, the listening experience can be quite varied and particularly challenging in noisy environments.
Modern cochlear implants drive electrodes with pulsatile signals (i.e., discrete stimulation pulses) that are computed by external sound processors. The main challenge still facing the CI field is how to best process sounds — to convert sounds to pulses on electrodes — in a way that makes them more intelligible to users. Recently, to stimulate progress on this problem, scientists in industry and academia organized a CI Hackathon to open the problem up to a wider range of ideas.
In this post, we share exploratory research demonstrating that a speech enhancement preprocessor — specifically, a noise suppressor — can be used at the input of a CI’s processor to enhance users’ understanding of speech in noisy environments. We also discuss how we built on this work in our entry for the CI Hackathon and how we will continue developing this work.
Improving CIs with Noise Suppression In 2019, a small internal project demonstrated the benefits of noise suppression at the input of a CI’s processor. In this project, participants listened to 60 pre-recorded and pre-processed audio samples and ranked them by their listening comfort. CI users listened to the audio using their devices' existing strategy for generating electrical pulses.
As shown below, both listening comfort and intelligibility usually increased, sometimes dramatically, when speech with noise (the lightest bar) was processed with noise suppression.
For the CI Hackathon, we built on the project above, continuing to leverage our use of a noise suppressor while additionally exploring an approach to compute the pulses too
Overview of the Processing Approach The hackathon considered a CI with 16 electrodes. Our approach decomposes the audio into 16 overlapping frequency bands, corresponding to the positions of the electrodes in the cochlea. Next, because the dynamic range of sound easily spans multiple orders of magnitude more than what we expect the electrodes to represent, we aggressively compress the dynamic range of the signal by applying "per-channel energy normalization" (PCEN). Finally, the range-compressed signals are used to create the electrodogram (i.e., what the CI displays on the electrodes).
In addition, the hackathon required a submission be evaluated in multiple audio categories, including music, which is an important but notoriously difficult category of sounds for CI users to enjoy. However, the speech enhancement network was trained to suppress non-speech sounds, including both noise and music, so we needed to take extra measures to avoid suppressing instrumental music (note that in general, music suppression might be preferred by some users in certain contexts). To do this, we created a “mix” of the original audio with the noise-suppressed audio so that enough of the music would pass through to remain audible. We varied in real-time the fraction of original audio mixed from 0% to 40% (0% if all of the input is estimated as speech, up to 40% as more of the input is estimated as non-speech) based on the estimate from the open-source YAMNet classifier on every ~1 second window of audio of whether the input is speech or non-speech.
The Conv-TasNet Speech Enhancement Model To implement a speech enhancement module that suppresses non-speech sounds, such as noise and music, we use the Conv-TasNet model, which can separate different kinds of sounds. To start, the raw audio waveforms are transformed and processed into a form that can be used by a neural network. The model transforms short, 2.5 millisecond frames of input audio with a learnable analysis transform to generate features optimized for sound separation. The network then produces two “masks” from those features: one mask for speech and one mask for noise. These masks indicate the degree to which each feature corresponds to either speech or noise. Separated speech and noise are reconstructed back to the audio domain by multiplying the masks with the analysis features, applying a synthesis transform back to audio-domain frames, and stitching the resulting short frames together. As a final step, the speech and noise estimates are processed by a mixture consistency layer, which improves the quality of the estimated waveforms by ensuring that they sum up to the original input mixture waveform.
The model is both causal and low latency: for each 2.5 milliseconds of input audio, the model produces estimates of separated speech and noise, and thus could be used in real-time. For the hackathon, to demonstrate what could be possible with increased compute power in future hardware, we chose to use a model variant with 2.9 million parameters. This model size is too large to be practically implemented in a CI today, but demonstrates what kind of performance would be possible with more capable hardware in the future.
Listening to the Results As we optimized our models and overall solution, we used the hackathon-provided vocoder (which required a fixed temporal spacing of electrical pulses) to produce audio simulating what CI users might perceive. We then conducted blind A-B listening tests as typical hearing users.
Listening to the vocoder simulations below, the speech in the reconstructed sounds — from the vocoder processing the electrodograms — is reasonably intelligible when the input sound doesn't contain too much background noise, however there is still room to improve the clarity of the speech. Our submission performed well in the speech-in-noise category and achieved second place overall.
A bottleneck on quality is that the fixed temporal spacing of stimulation pulses sacrifices fine-time structure in the audio. A change to the processing to produce pulses timed to peaks in the filtered sound waveforms captures more information about the pitch and structure of sound than is conventionally represented in implant stimulation patterns.
It's important to note that this second vocoder output is overly optimistic about how well it might sound to a real CI user. For instance, the simple vocoder used here does not model how current spread in the cochlea blurs the stimulus, making it harder to resolve different frequencies. But this at least suggests that preserving fine-time structure is valuable and that the electrodogram itself is not the bottleneck.
Ideally, all processing approaches would be evaluated by a broad range of CI users, with the electrodograms implemented directly on their CIs rather than relying upon vocoder simulations.
Conclusion and a Call to Collaborate We are planning to follow up on this experience in two main directions. First, we plan to explore the application of noise suppression to other hearing-accessibility modalities, including hearing aids, transcription, and vibrotactile sensory substitution. Second, we'll take a deeper dive into the creation of electrodogram patterns for cochlear implants, exploiting fine temporal structure that is not accommodated in the usual CIS (continous interleaved sampling) patterns that are standard in the industry. According to Louizou: “It remains a puzzle how some single-channel patients can perform so well given the limited spectral information they receive''. Therefore, using fine temporal structure might be a critical step towards achieving an improved CI experience.
Google is committed to building technology with and for people with disabilities. If you are interested in collaborating to improve the state of the art in cochlear implants (or hearing aids), please reach out to ci-collaborators@googlegroups.com.
Acknowledgements We would like to thank the Cochlear Impact hackathon organizers for giving us this opportunity and partnering with us. The participating team within Google is Samuel J. Yang, Scott Wisdom, Pascal Getreuer, Chet Gnegy, Mihajlo Velimirović, Sagar Savla, and Richard F. Lyon with guidance from Dan Ellis and Manoj Plakal.
The intensive care unit (ICU) of a hospital looks after the most medically vulnerable patients, many of whom require organ support, such as mechanical ventilation or dialysis. While always critical, the demand on ICU services during the COVID-19 pandemic has further underscored the importance of data-driven decision-making in healthcare. Furthermore, the ability to accurately predict the clinical outcomes of ICU patients has the potential to guide therapy and may inform decisions about most effective care, including staffing and triage support.
Applying machine learning (ML) to electronic health records (EHRs) has shown promise in predicting clinical outcomes. However, many of these ML models are based on single-task learning (ST), where the models are trained only to predict a specific adverse event, such as an organ dysfunction or the need for a life-support intervention. Of greater benefit would be to train multi-task models, which take into account a variety of competing risks along with the interdependencies between organ systems that factor into patient outcomes in a realistic setting.
In “Multi-task prediction of organ dysfunction in the ICU using sequential sub-network routing”, we propose a multi-task learning (MTL) architecture, called Sequential Sub-Network Routing (SeqSNR), that better captures the complexity of a realistic setting. Inspired by a clinician's holistic approach to diagnosing problems, SeqSNR is designed to use flexible parameter sharing and routing to find related tasks and encourage cross-learning between them. We successfully applied SeqSNR to the task of continuous adverse event prediction in an ICU setting and showed advantages over single-task and naïve multi-tasking, especially in low training data scenarios.
Data and Labels In this study, we used the freely available, open access, de-identified MIMIC-III EHR dataset, which includes a patient cohort consisting of 36,498 adults across 52,038 critical care admissions at the Beth Israel Deaconess Medical Center between 2001 and 2012. Similar to our previous studies, we employed a version of the MIMIC-III dataset that was mapped to the Fast Healthcare Interoperability Resource (FHIR) standard and used a comprehensive set of features, including a sequence of vital signs, laboratory results, past medications, procedures, diagnoses, and more.
The MIMIC-III database contains multi-modal recordings from ICU patients. Unlike most datasets in ML, the input and targets are often not explicitly defined and must be inferred from the data. So, using a combination of automated rule-based methods and clinical review, we defined a suite of diverse endpoints, including critical care interventions, specific organ dysfunctions, and overall patient outcomes.
The task given to the model was to predict the onset of a selection of adverse events within 24–48 hours for every hour after a patient’s admission into the ICU. The defined adverse events included acute kidney injury (AKI), continuous renal replacement therapy (CRRT) dialysis, administration of vasopressors and inotropes, mechanical ventilation (MV), mortality, and remaining length of stay (LoS).
The SeqSNR Algorithm While multi-task learning captures the interdependencies between organ systems and balances competing risks, it can be challenging to implement successfully. In practice, jointly-trained tasks often impair one another, an effect called “negative transfer”. The intuition behind SeqSNR was that modular ‘sub-networks’ would mitigate this issue by automatically optimizing how information is shared across multiple tasks.
SeqSNR is a time series adaptation of the SNR architecture and is a combination of a deep embedding layer followed by stacked recurrent neural network (RNN) layers. Modularisation is achieved by splitting both the embedding layer and the RNN stack into multiple modules connected by routing variables that are learned during the training phase. The routing connections are always created between blocks in one layer and the next. This approach minimizes negative transfer by ensuring that data of low relevance to a particular task layer is filtered out. In essence, this means that each task utilizes a different path through the model.
Findings SeqSNR shows a modest improvement in discriminative performance overall relative to single-task and naïve multitasking. However, it's performance improvement is more significant in scenarios with few training labels.
Because the prevalence of different outcomes varied widely in the dataset (e.g. ~38% of patients had MV, but CRRT dialysis is present for only ~3%), many accuracy metrics are not suitable. Instead, we report the area under the precision recall curve (AU PRC), which is more reliable given imbalanced data. Moreover, we performed the Wilcoxon Signed Rank Tests to draw statistically significant conclusions for pairwise comparisons of ST learning, shared-bottom (SB) multi-task learning (i.e., naïve multi-task learning), and SeqSNR across bootstrapped samples from the held-out test set. The performance differences between the three architectures were modest, but SeqSNR outperformed both ST and SB in four out of six tasks (p-values are reported in the paper).
Label Efficiency We hypothesized that multi-task learning could assist in low-data scenarios by using easy-to-label auxiliary tasks to boost the performance of the main tasks. We formulated prediction tasks with only a portion of the training labels available for the primary prediction task, but kept the entire dataset for the “helper tasks”. The latter are chosen because they are reliably encoded in the EHR and are straightforward to timestamp. An example of such a helper task is length of stay, since the start and end of admissions are accurately timestamped in MIMIC-III. On the other hand, the start and end of mechanical ventilation events are not reliably timestamped. So, we defined a set of rules based on expert-defined heuristics to determine the ventilation times using multiple sources of mechanical ventilator–related settings along with physiological measurements in the EHR dataset that are indicative of MV.
The development of these rules for a new clinical endpoint was time-consuming and involved manual review of the dataset by experts. The difficulty in exhaustively labeling the dataset led us to test the model performance with only 1–10% of the data labeled, which resulted in a decline in model performance. The “helper tasks” are useful in this scenario since they are 100% labeled and can be used with the primary tasks (1–10% labeled) to jointly train the multi-task model for improved overall performance.
We chose AKI, mechanical ventilation, CRRT Dialysis, and vasoactive medications as primary endpoints using 1%, 5%, and 10% of the training labels, along with 100% of labels for the helper tasks — labs and vitals, mortality, and LoS. Performance of both ST and SeqSNR decreased as the percentage of labels for the primary endpoint was reduced, but SeqSNR outperformed ST across all tasks and all training data reduction percentages, with a statistically significant boost in performance for all cases.
This is a useful finding, given the difficulties of annotating endpoint labels in EHR datasets, which frequently necessitates human evaluation by doctors. The ability to use numerous endpoints, some of which may be easier to label (like duration of stay or mortality), could lessen the need for manual curation on more difficult endpoints that are annotated differently (like mechanical ventilation).
Subgroup Performance While the version of the MIMIC-III dataset used contained labels for gender and age, it did not contain information on race and the information on ethnicity was limited. We computed the performance of all selected models across age and gender subgroups. We observed that in the scenarios with few instances in the dataset, the MTL models (both SB models and SeqSNR) often outperform ST. Even though there are exceptions, on average all models seem to be relatively balanced across age and gender subgroups. We invite the reader to refer to the supplemental section of our paper for a detailed performance breakdown.
Next Steps This work is a proof of concept for SeqSNR on a set of canonical EHR prediction tasks. The code for this architecture is publicly available here. And will hopefully stimulate further research in EHR multi-tasking and other deep learning architectures inspired by clinical reasoning.
In future, it will be important to evaluate the performance of SeqSNR on different combinations of tasks, different time horizons and different datasets. One other area of potential growth in this project is to expand subgroup analysis by including datasets with additional population information, race, ethnicity, etc. Another area we are exploring is expanding subgroup analysis by including datasets with additional population information, such as race, ethnicity, etc. We also emphasize that these are prototype models designed to showcase methodologies, and more rigorous evaluation would be needed to bring these tools into deployment.
Acknowledgements This work involved collaborative efforts from a multidisciplinary team of researchers, software engineers, clinicians, and cross-functional contributors. We thank our co-authors: Eric Loreaux, Anne Mottram, Ivan Protsyuk, Natalie Harris, Sebastien Baur, Yuan Xue, Jessica Schrouff, Ali Connell, Alan Karthikesalingam, Martin Seneviratne from Google, Nenad Tomasev from Deepmind, and Hugh Montgomery from University College London. We also thank Zhe Zhao from Google Research and Kathryn Rough, Cian Hughes, Megumi Morigami and Doris Wong from Google Health for their input and review, and the MIMIC team for curating this open access dataset for the research community.
Groups across Google are actively pursuing research across the field of machine learning, ranging from theory to application. With scalable tools and architectures, we build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, and more.
Google is proud to be a Platinum Sponsor of the thirty-eighth International Conference on Machine Learning (ICML 2021), a premier annual event happening this week. As a leader in machine learning research — with over 100 accepted publications and Googlers participating in workshops — we look forward to our continued partnership with the broader machine learning research community.
Registered for ICML 2021? We hope you’ll visit the Google virtual booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Take a look below to learn more about the Google research being presented at ICML 2021 (Google affiliations in bold).
Organizing Committee ICML Board Members include: Corinna Cortes, Hugo Larochelle, Shakir Mohamed ICML Emeritus Board includes: William Cohen, Andrew McCallum Tutorial Co-Chair member: Quoc Lee
Publications Attention Is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Scalable Evaluation of Multi-agent Reinforcement Learning with Melting Pot Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel
On the Optimality of Batch Policy Optimization Algorithms Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li*, Csaba Szepesvari, Dale Schuurmans
Low-Rank Sinkhorn Factorization Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
PID Accelerated Value Iteration Algorithm Amir-Massoud Farahmand, Mohammad Ghavamzadeh
Dueling Convex Optimization Aadirupa Saha, Tomer Koren, Yishay Mansour
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
Offline Reinforcement Learning with Pseudometric Learning Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
Revisiting Rainbow: Promoting More Insightful and Inclusive Deep Reinforcement Learning Research (see blog post) Johan S. Obando-Ceron, Pablo Samuel Castro
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL Seyed Kamyar Seyed Ghasemipour*, Dale Schuurmans, Shixiang Shane Gu
Variational Data Assimilation with a Learned Inverse Observation Operator Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer
Tilting the Playing Field: Dynamical Loss Functions for Machine Learning Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu
Model-Based Reinforcement Learning via Latent-Space Collocation Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine
Momentum Residual Neural Networks Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
OmniNet: Omnidirectional Representations from Transformers Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler
Synthesizer: Rethinking Self-Attention for Transformer Models Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng
Towards Domain-Agnostic Contrastive Learning Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le
Randomized Entity-wise Factorization for Multi-agent Reinforcement Learning Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, Christian Szegedy
Emergent Social Learning via Multi-agent Reinforcement Learning Kamal Ndousse, Douglas Eck, Sergey Levine, Natasha Jaques
Improved Contrastive Divergence Training of Energy-Based Models Yilun Du, Shuang Li, Joshua Tenenbaum, Igor Mordatch
Characterizing Structural Regularities of Labeled Data in Overparameterized Models Ziheng Jiang*, Chiyuan Zhang, Kunal Talwar, Michael Mozer
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar
EfficientNetV2: Smaller Models and Faster Training Mingxing Tan, Quoc V. Le
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies Paul Vicol, Luke Metz, Jascha Sohl-Dickstein
Federated Composite Optimization Honglin Yuan*, Manzil Zaheer, Sashank Reddi
Light RUMs Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Catformer: Designing Stable Transformers via Sensitivity Analysis Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang
Representation Matters: Offline Pretraining for Sequential Decision Making Mengjiao Yang, Ofir Nachum
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning Jongwook Choi*, Archit Sharma*, Honglak Lee, Sergey Levine, Shixiang Shane Gu
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization Wesley Chung, Valentin Thomas, Marlos C. Machado, Nicolas Le Roux
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers Piotr Teterwak*, Chiyuan Zhang, Dilip Krishnan, Michael C. Mozer
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu
Hyperparameter Selection for Imitation Learning Leonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphael Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo
Debiasing a First-Order Heuristic for Approximate Bi-Level Optimization Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, Adrian Weller
Characterizing the Gap Between Actor-Critic and Policy Gradient Junfeng Wen, Saurabh Kumar, Ramki Gummadi, Dale Schuurmans
Composing Normalizing Flows for Inverse Problems Jay Whang, Erik Lindgren, Alexandros Dimakis
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret Asaf Cassel, Tomer Koren
Learning to Price Against a Moving Target Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah
Fairness and Bias in Online Selection Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard
The Impact of Record Linkage on Learning from Feature Partitioned Data Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Jakub Nabaglo, Giorgio Patrini, Guillaume Smith, Brian Thorne
Reserve Price Optimization for First Price Auctions in Display Advertising Zhe Feng*, Sébastien Lahaie, Jon Schneider, Jinchao Ye
A Regret Minimization Approach to Iterative Learning Control Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh
A Statistical Perspective on Distillation Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar
Best Model Identification: A Rested Bandit Formulation Leonardo Cella, Massimiliano Pontil, Claudio Gentile
Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith
Stochastic Multi-armed Bandits with Unrestricted Delay Distributions Tal Lancewicki, Shahar Segal, Tomer Koren, Yishay Mansour
Regularized Online Allocation Problems: Fairness and Beyond Santiago Balseiro, Haihao Lu, Vahab Mirrokni
Implicit Rate-Constrained Optimization of Non-decomposable Objectives Abhishek Kumar, Harikrishna Narasimhan, Andrew Cotter
Leveraging Non-uniformity in First-Order Non-Convex Optimization Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Dynamic Balancing for Model Selection in Bandits and RL Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit
Adversarial Dueling Bandits Aadirupa Saha, Tomer Koren, Yishay Mansour
Optimizing Black-Box Metrics with Iterative Example Weighting Gaurush Hiranandani*, Jatin Mathur, Harikrishna Narasimhan, Mahdi Milani Fard, Oluwasanmi Koyejo
Relative Deviation Margin Bounds Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
MC-LSTM: Mass-Conserving LSTM Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer
12-Lead ECG Reconstruction via Koopman Operators Authors:Tomer Golany, Kira Radinsky, Daniel Freedman, Saar Minha
Finding Relevant Information via a Discrete Fourier Expansion Mohsen Heidari, Jithin Sreedharan, Gil Shamir, Wojciech Szpankowski
LEGO: Latent Execution-Guided Reasoning for Multi-hop Question Answering on Knowledge Graphs Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Michihiro Yasunaga, Haitian Sun, Dale Schuurmans, Jure Leskovec, Denny Zhou
SpreadsheetCoder: Formula Prediction from Semi-structured Context Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou
Combinatorial Blocking Bandits with Stochastic Delays Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramani, Sanjay Shakkottai
Beyond log2(T) Regret for Decentralized Bandits in Matching Markets Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman
Robust Pure Exploration in Linear Bandits with Limited Budget Ayya Alieva, Ashok Cutkosky, Abhimanyu Das
Latent Programmer: Discrete Latent Codes for Program Synthesis Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (see blog post) Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig
On Linear Identifiability of Learned Representations Geoffrey Roeder, Luke Metz, Diederik P. Kingma
Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia M Procopiuc, Claudio Gentile
Differentially Private Quantiles Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Active Covering Heinrich Jiang, Afshin Rostamizadeh
Sharf: Shape-Conditioned Radiance Fields from a Single View Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari
Learning a Universal Template for Few-Shot Dataset Generalization Eleni Triantafillou*, Hugo Larochelle, Richard Zemel, Vincent Dumoulin
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Differentially-Private Clustering of Easy Instances Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Label-Only Membership Inference Attacks Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
Neural Feature Matching in Implicit 3D Representations Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves
Locally Private k-Means in One Round Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Large-Scale Meta-learning with Continual Trajectory Shifting Jaewoong Shin, Hae Beom Lee, Boqing Gong, Sung Ju Hwang
Statistical Estimation from Dependent Data Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
Oneshot Differentially Private Top-k Selection Gang Qiao, Weijie J. Su, Li Zhang
Unsupervised Part Representation by Flow Capsules Sara Sabour, Andrea Tagliasacchi, Soroosh Yazdani, Geoffrey E. Hinton, David J. Fleet
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
Practical and Private (Deep) Learning Without Sampling or Shuffling Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha
Leveraging Public Data for Practical Private Query Release Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Zhiwei Steven Wu
Meta-Thompson Sampling Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári
Implicit-PDF: Non-parametric Representation of Probability Distributions on the Rotation Manifold Kieran A Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
Improving Ultrametrics Embeddings Through Coresets Vincent Cohen-Addad, Rémi de Joannis de Verclos, Guillaume Lagarde
A Discriminative Technique for Multiple-Source Adaptation Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh, Ningshan Zhang
Self-Supervised and Supervised Joint Training for Resource-Rich Machine Translation Yong Cheng, Wei Wang*, Lu Jiang, Wolfgang Macherey
Correlation Clustering in Constant Many Parallel Rounds Vincent Cohen-Addad, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi
Meta-learning Bidirectional Update Rules Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Aguera y Arcas
Discretization Drift in Two-Player Games Mihaela Rosca, Yan Wu, Benoit Dherin, David G.T. Barrett
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations Haitian Sun*, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William W. Cohen
Learn2Hop: Learned Optimization on Rough Landscapes Amil Merchant, Luke Metz, Samuel Schoenholz, Ekin Cubuk
Locally Adaptive Label Smoothing Improves Predictive Churn Dara Bahri, Heinrich Jiang
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization Patrick H. Chen, Wei Wei, Cho-jui Hsieh, Bo Dai
Workshops (only Google affiliations are noted) LatinX in AI (LXAI) Research at ICML 2021 Hosts: Been Kim, Natasha Jaques
Uncertainty and Robustness in Deep Learning Organizers: Balaji Lakshminarayanan, Jasper Snoek Invited Speaker: Dustin Tran
Reinforcement Learning for Real Life Organizers: Minmin Chen, Lihong Li Invited Speaker: Ed Chi
Interpretable Machine Learning in Healthcare Organizers: Alan Karthikesalingam Invited Speakers: Abhijit Guha Roy, Jim Winkens
The Neglected Assumptions in Causal Inference Organizer: Alexander D'Amour
ICML Workshop on Algorithmic Recourse Invited Speakers: Been Kim, Berk Ustun
A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning Invited Speaker: Nicholas Carlini
Overparameterization: Pitfalls and Opportunities Organizers: Yasaman Bahri, Hanie Sedghi
Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3) Invited Speaker: Thomas Steinke
Beyond First-Order Methods in Machine Learning Systems Invited Speaker: Courtney Paquette
ICML 2021 Workshop: Self-Supervised Learning for Reasoning and Perception Invited Speaker: Chelsea Finn
Workshop on Reinforcement Learning Theory Invited Speaker: Bo Dai
Tutorials (only Google affiliations are noted) Responsible AI in Industry: Practical Challenges and Lessons Learned Organizers: Ben Packer
Online and Non-stochastic Control Organizers: Elad Hazan
Random Matrix Theory and ML (RMT +ML) Organizers: Fabian Pedregosa, Jeffrey Pennington, Courntey Paquette Self-Attention for Computer Vision Organizers: Prajit Ramachandran, Ashish Vaswani
* Indicates work done while at Google
Natural image synthesis is a broad class of machine learning (ML) tasks with wide-ranging applications that pose a number of design challenges. One example is image super-resolution, in which a model is trained to transform a low resolution image into a detailed high resolution image (e.g., RAISR). Super-resolution has many applications that can range from restoring old family portraits to improving medical imaging systems. Another such image synthesis task is class-conditional image generation, in which a model is trained to generate a sample image from an input class label. The resulting generated sample images can be used to improve performance of downstream models for image classification, segmentation, and more.
Generally, these image synthesis tasks are performed by deep generative models, such as GANs, VAEs, and autoregressive models. Yet each of these generative models has its downsides when trained to synthesize high quality samples on difficult, high resolution datasets. For example, GANs often suffer from unstable training and mode collapse, and autoregressive models typically suffer from slow synthesis speed.
Alternatively, diffusion models, originally proposed in 2015, have seen a recent revival in interest due to their training stability and their promising sample quality results on image and audio generation. Thus, they offer potentially favorable trade-offs compared to other types of deep generative models. Diffusion models work by corrupting the training data by progressively adding Gaussian noise, slowly wiping out details in the data until it becomes pure noise, and then training a neural network to reverse this corruption process. Running this reversed corruption process synthesizes data from pure noise by gradually denoising it until a clean sample is produced. This synthesis procedure can be interpreted as an optimization algorithm that follows the gradient of the data density to produce likely samples.
Today we present two connected approaches that push the boundaries of the image synthesis quality for diffusion models — Super-Resolution via Repeated Refinements (SR3) and a model for class-conditioned synthesis, called Cascaded Diffusion Models (CDM). We show that by scaling up diffusion models and with carefully selected data augmentation techniques, we can outperform existing approaches. Specifically, SR3 attains strong image super-resolution results that surpass GANs in human evaluations. CDM generates high fidelity ImageNet samples that surpass BigGAN-deep and VQ-VAE2 on both FID score and Classification Accuracy Score by a large margin.
SR3: Image Super-Resolution SR3 is a super-resolution diffusion model that takes as input a low-resolution image, and builds a corresponding high resolution image from pure noise. The model is trained on an image corruption process in which noise is progressively added to a high-resolution image until only pure noise remains. It then learns to reverse this process, beginning from pure noise and progressively removing noise to reach a target distribution through the guidance of the input low-resolution image..
With large scale training, SR3 achieves strong benchmark results on the super-resolution task for face and natural images when scaling to resolutions 4x–8x that of the input low-resolution image. These super-resolution models can further be cascaded together to increase the effective super-resolution scale factor, e.g., stacking a 64x64 → 256x256 and a 256x256 → 1024x1024 face super-resolution model together in order to perform a 64x64 → 1024x1024 super-resolution task.
We compare SR3 with existing methods using human evaluation study. We conduct a Two-Alternative Forced Choice Experiment where subjects are asked to choose between the reference high resolution image, and the model output when asked the question, “Which image would you guess is from a camera?” We measure the performance of the model through confusion rates (% of time raters choose the model outputs over reference images, where a perfect algorithm would achieve a 50% confusion rate). The results of this study are shown in the figure below.
CDM: Class-Conditional ImageNet Generation Having shown the effectiveness of SR3 in performing natural image super-resolution, we go a step further and use these SR3 models for class-conditional image generation. CDM is a class-conditional diffusion model trained on ImageNet data to generate high-resolution natural images. Since ImageNet is a difficult, high-entropy dataset, we built CDM as a cascade of multiple diffusion models. This cascade approach involves chaining together multiple generative models over several spatial resolutions: one diffusion model that generates data at a low resolution, followed by a sequence of SR3 super-resolution diffusion models that gradually increase the resolution of the generated image to the highest resolution. It is well known that cascading improves quality and training speed for high resolution data, as shown by previous studies (for example in autoregressive models and VQ-VAE-2) and in concurrent work for diffusion models. As demonstrated by our quantitative results below, CDM further highlights the effectiveness of cascading in diffusion models for sample quality and usefulness in downstream tasks, such as image classification.
Along with including the SR3 model in the cascading pipeline, we also introduce a new data augmentation technique, which we call conditioning augmentation, that further improves the sample quality results of CDM. While the super-resolution models in CDM are trained on original images from the dataset, during generation they need to perform super-resolution on the images generated by a low-resolution base model, which may not be of sufficiently high quality in comparison to the original images. This leads to a train-test mismatch for the super-resolution models. Conditioning augmentation refers to applying data augmentation to the low-resolution input image of each super-resolution model in the cascading pipeline. These augmentations, which in our case include Gaussian noise and Gaussian blur, prevents each super-resolution model from overfitting to its lower resolution conditioning input, eventually leading to better higher resolution sample quality for CDM.
Altogether, CDM generates high fidelity samples superior to BigGAN-deep and VQ-VAE-2 in terms of both FID score and Classification Accuracy Score on class-conditional ImageNet generation. CDM is a pure generative model that does not use a classifier to boost sample quality, unlike other models such as ADM and VQ-VAE-2. See below for quantitative results on sample quality.
Conclusion With SR3 and CDM, we have pushed the performance of diffusion models to state-of-the-art on super-resolution and class-conditional ImageNet generation benchmarks. We are excited to further test the limits of diffusion models for a wide variety of generative modeling problems. For more information on our work, please visit Image Super-Resolution via Iterative Refinement and Cascaded Diffusion Models for High Fidelity Image Generation.
Acknowledgements: We thank our co-authors William Chan, Mohammad Norouzi, Tim Salimans, and David Fleet, and we are grateful for research discussions and assistance from Ben Poole, Jascha Sohl-Dickstein, Doug Eck, and the rest of the Google Research, Brain Team. Thanks to Tom Small for helping us with the animations.
