Google AI Blog

Fluid Annotation: An Exploratory Machine Learning–Powered Interface for Faster Image Annotation

Monday, October 22, 2018

Posted by Jasper Uijlings and Vittorio Ferrari, Senior Research Scientists, Machine PerceptionTensorFlow Object Detection APIOpen Imagessemantic segmentationtraditional manual labelingCOCOStuff

Example of image in the COCO dataset (left) and its pixel-wise semantic labeling (right). Image credit: Florida Memory, original image.

Fluid Annotation: A Human-Machine Collaboration Interface for Full Image AnnotationBrave New Ideas2018 ACM Multimedia Conference

Visualization of the fluid annotation interface in action on image from COCO dataset. Image credit: gamene, original image.

Mask-RCNNtry out the demo

Comparison of annotations using traditional manual labeling tools (middle column) and fluid annotation (right) on three COCO images. While object boundaries are often more accurate when using manual labeling tools, the biggest source of annotation differences is because human annotators often disagree on the exact object class. Image Credits: sneaka, original image (top), Dan Hurt, original image (middle), Melodie Mesiano, original image (bottom).

AcknowledgementsThis work was done in collaboration with Misha Andriluka. Special thanks to Christine Sugrue for creating the fluid annotation demo. We also thank Anna Ukhanova and Damien Henry for their valuable input.

See Better and Further with Super Res Zoom on the Pixel 3

Monday, October 15, 2018

Posted by Bartlomiej Wronski, Software Engineer and Peyman Milanfar, Lead Scientist, Computational ImagingDSLR camerassinglemany framesSuper Res Zoom means that if you pinch-zoom before pressing the shutter, you’ll get a lot more details in your picture than if you crop afterwards.

Crops of 2x Zoom: Pixel 2, 2017 vs. Super Res Zoom on the Pixel 3, 2018.

The Challenges of Digital Zoomlinear interpolation methodsRAISRColor Filter Arrays and Demosaicingdemosaicingcolor filter arrayBayer

A Bayer mosaic color filter. Every 2x2 group of pixels captures light filtered by a specific color — two green pixels (because our eyes are more sensitive to green), one red, and one blue. This pattern is repeated across the whole image.

Demosaicing reconstructs missing color information by using neighboring neighboring pixels.

From Burst Photography to Multi-frame Super-resolutionburst photographyHDR+ algorithmknown for more than a decadedrizzlemultiplesuper-resolution

As compared to the standard demosaicing pipeline that needs to interpolate the missing colors (top), ideally, one could fill some holes from multiple images, each shifted by one pixel horizontally or vertically.

Image AliasingAliasingMoiré patternsunfortunate choice of wardrobe

Left: High-resolution, single image of a table edge against a high frequency patterned background, Right: Different frames from a burst. Aliasing and Moiré effects are visible between different frames — pixels seem to jump around and produce different colored patterns.

Practical Super-resolution Using Hand MotionOptical Image Stabilization

Effect of hand tremor as seen in a cropped burst, after global alignment.

Overcoming the Challenges of Super-resolution

A single image from a burst is noisy, even in good lighting. A practical super-resolution algorithm needs to be aware of this noise and work correctly despite it. We don’t want to get just a higher resolution noisy image - our goal is to both increase the resolution but also produce a much less noisy result.

Left: Single frame frame from a burst taken in good light conditions can still contain a substantial amount of noise due to underexposure. Right: Result of merging multiple frames after burst processing.

Motion between images in a burst is not limited to just the movement of the camera. There can be complex motions in the scene such as wind-blown leaves, ripples moving across the surface of water, cars, people moving or changing their facial expressions, or the flicker of a flame — even some movements that cannot be assigned a single, unique motion estimate because they are transparent or multi-layered, such as smoke or glass. Completely reliable and localized alignment is generally not possible, and therefore a good super-resolution algorithm needs to work even if motion estimation is imperfect.

Because much of motion is random, even if there is good alignment, the data may be dense in some areas of the image and sparse in others. The crux of super-resolution is a complex interpolation problem, so the irregular spread of data makes it challenging to produce a higher-resolution image in all parts of the grid.

To effectively merge frames in a burst, and to produce a red, green, and blue value for every pixel without the need for demosaicing, we developed a method of integrating information across the frames that takes into account the edges of the image, and adapts accordingly. Specifically, we analyze the input frames and adjust how we combine them together, trading off increase in detail and resolution vs. noise suppression and smoothing. We accomplish this by merging pixels along the direction of apparent edges, rather than across them. The net effect is that our multi-frame method provides the best practical balance between noise reduction and enhancement of details.

Left: Merged image with sub-optimal tradeoff of noise reduction and enhanced resolution. Right: The same merged image with a better tradeoff.

To make the algorithm handle scenes with complex local motion (people, cars, water or tree leaves moving) reliably, we developed a robustness model that detects and mitigates alignment errors. We select one frame as a “reference image”, and merge information from other frames into it only if we’re sure that we have found the correct corresponding feature. In this way, we can avoid artifacts like “ghosting” or motion blur, and wrongly merged parts of the image.

A fast moving bus in a burst of images. Left: Merge without robustness model. Right: Merge with robustness model.

Pushing the State of the Art in Mobile PhotographyPortrait modeHDR+album

Left: Crop of 7x zoomed image on Pixel 2. Right: Same crop from Super Res Zoom on Pixel 3.

An illustrative animation of Super Res Zoom. When the user takes a zoomed photo, the Pixel 3 takes advantage of the user’s natural hand motion and captures a burst of images at subtly different positions. These are then merged together to add detail to the final image.AcknowledgementsSuper Res Zoom is the result of a collaboration across several teams at Google. The project would not have been possible without the joint efforts of teams managed by Peyman Milanfar, Marc Levoy, and Bill Freeman. The authors would like to thank Marc Levoy and Isaac Reynolds in particular for their assistance in the writing of this blog.

The authors wish to especially acknowledge the following key contributors to the Super Res Zoom project: Ignacio Garcia-Dorado, Haomiao Jiang, Manfred Ernst, Michael Krainin, Daniel Vlasic, Jiawen Chen, Pascal Getreuer, and Chia-Kai Liang. The project also benefited greatly from contributions and feedback by Ce Liu, Damien Kelly, and Dillon Sharlet. How to get the most out of Super Res Zoom?

Pinch and zoom, or use the + button to increase zoom by discrete steps.

Double-tap the preview to quickly toggle between zoomed in and zoomed out.

Super Res works well at all zoom factors, though for performance reasons, it activates only above 1.2x. That’s about half way between no zoom and the first “click” in the zoom UI.

There are fundamental limits to the optical resolution of a wide-angle camera. So to get the most out of (any) zoom, keep the magnification factor modest.

Avoid fast moving objects. Super Res Zoom will capture them correctly, but you will not likely get increased resolution.

* It’s worth noting that the situation is similar in some ways to how we see — in human (and other mammalian) eyes, different eye cone cells are sensitive to some specific colors, with the brain filling in the details to reconstruct the full image.^↩

Applying Deep Learning to Metastatic Breast Cancer Detection

Friday, October 12, 2018

Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Healthcare, Google AImetastasizedTNM cancer stagingabout 1 in 4as low as 38%deep learning–based approach to improve diagnostic accuracy2016 ISBI Camelyon Challengesignificantly higher cancer detection ratesArtificial Intelligence Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for PathologistsArchives of Pathology and Laboratory MedicineImpact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast CancerThe American Journal of Surgical Pathologyfirst paperthe Naval Medical Center San Diego

Left: sample view of a slide containing lymph nodes, with multiple artifacts: the dark zone on the left is an air bubble, the white streaks are cutting artifacts, the red hue across some regions are hemorrhagic (containing blood), the tissue is necrotic (decaying), and the processing quality was poor. Right: LYNA identifies the tumor region in the center (red), and correctly classifies the surrounding artifact-laden regions as non-tumor (blue).

second paper

Left: sample views of a slide containing lymph nodes with a small metastatic breast tumor at progressively higher magnifications. Right: the same views when shown with algorithmic “assistance” (LYmph Node Assistant, LYNA) outlining the tumor in cyan.

Open Sourcing Active Question Reformulation with Reinforcement Learning

Wednesday, October 10, 2018

Posted by Michelle Chen Huebscher, Software Engineer and Rodrigo Nogueira, New York University PhD Student and Software Engineering Intern, Google AI LanguageNatural language understandingongoing focusmachine translationsyntacticsemanticmuch morebuilding blockTensorFlow package for Active Question Answeringreinforcement learningICLR 2018Ask the Right Questions: Active Question Reformulation with Reinforcement LearningActive Question Answeringsupervised learning techniquesWhen was Tesla born?When is Tesla’s birthdayWhich year was Tesla bornJuly 10 1856

reinforcement learningour paperpolicytf-idf query term re-weightingword stemmingBuild Your Own ActiveQA System

A pretrained sequence to sequence model that takes as input a question and returns its reformulations. This task is similar to machine translation, translating from English to English, and indeed the initial model can be used for general paraphrasing. For its implementation we use and customize the TensorFlow Neural Machine Translation Tutorial code. We adapted the code to support training with reinforcement learning, using policy gradient methods.^*

An answer selection model. The answer selector uses a convolutional neural network and assigns a score to each triplet of original question, reformulation and answer. The selector uses pre-trained, publicly available word embeddings (GloVe).

A question answering system (the environment). For this purpose we use BiDAF, a popular question answering system, described in Seo et al. (2017).

AcknowledgmentsContributors to this research and release include Alham Fikri Aji, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Alexey Gronskiy, Neil Houlsby, Yannic Kilcher, and Wei Wang.
* The system we reported on in our paper used the TensorFlow sequence-to-sequence code used in Britz et al. (2017). Later, an open source version of the Google Translation model (GNMT) was published as a tutorial. The ActiveQA version released today is based on this more recent, and actively developed implementation. For this reason the released system varies slightly from the paper’s. Nevertheless, the performance and behavior are qualitatively and quantitatively comparable.^↩

Highlights from the Google AI Residency Program

Tuesday, October 9, 2018

Posted by Phing Lee, Program Manager, Google AI Residency
inaugural class of the Google Brain ResidencyGoogle AI ResidencyGoogle AI teams

Some of our 2017 Google AI residents at the 2017 Neural Information Processing Systems Conference, hosted in Long Beach, California.

machine learningroboticshealthcare

A study on the effect of adversarial examples on human visual perception.

An algorithm that enables robots to learn more safely by avoiding states from which they cannot reset.

Initialization methods which enable training of neural network with unprecedented depths of 10K+ layers.

A method to make training more scalable by using larger mini-batches, which when applied to ResNet-50 on ImageNet reduced training time without compromising test accuracy.

And many more...

This experiment demonstrated (for the first time) the susceptibility of human time-limited vision to adversarial examples. For more details, see “Adversarial Examples that Fool both Computer Vision and Time-Limited Humans” accepted at NIPS 2018).

An algorithm for safe reinforcement learning prevents robots from taking actions they cannot undo. For more details, see “Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning” (accepted at ICLR 2018).

Extremely deep CNNs can be trained without the use of any special tricks simply by using a specially designed (Delta-Orthogonal) initialization. Test (solid) and training (dashed) curves on MNIST (top) and CIFAR10 (bottom). For more details, see “Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks” accepted at ICML 2018.

Applying a sequence of simple scaling rules, we increase the SGD batch size and reduce the number of parameter updates required to train our model by an order of magnitude, without sacrificing test set accuracy. This enables us to dramatically reduce model training time. For more details, see “Don’t Decay the Learning Rate, Increase the Batch Size”, accepted at ICLR 2018.

global officesperceptionalgorithms and optimizationlanguagehealthcaremuch moreg.co/airesidency/applyg.co/airesidency

Introducing the Kaggle “Quick, Draw!” Doodle Recognition Challenge

Friday, September 28, 2018

Posted by Thomas Deselaers, Senior Staff Software Engineer and Jake Walker, Product Manager, Machine PerceptionOnline handwriting recognitionTranslateKeepHandwriting Inputimprove your drawing abilitiesbuild virtual worldsQuick, Draw!dataset of 50M drawings1B that were drawnmany different new projectsKaggle "Quick, Draw!" Doodle Recognition Challenge“Quick, Draw!” datasetThe Dataset

Correct: the user drew the prompted category and the computer only recognized it correctly after the user was done drawing.

Correct, but incomplete: the user drew the prompted category and the computer recognized it correctly before the user had finished. Incompleteness can vary from nearly ready to only a fraction of the category drawn. This is probably fairly common in images that are marked as recognized correctly.

Correct, but not recognized correctly: The player drew the correct category but the AI never recognized it. Some players react to this by adding more details. Others scribble out and try again.

Incorrect: some players have different concepts in mind when they see a word - e.g. in the category seesaw, we have observed a number of saw drawings.

Get Startedtutorialchallenge websitekernelsAcknowledgementsWe'd like to thank everyone who worked with us on this, particularly Jonas Jongejan and Brenda Fogg from the Creative Lab team, Julia Elliott and Walter Reade from the Kaggle team, and the handwriting recognition team.

Building Google Dataset Search and Fostering an Open Data Ecosystem

Wednesday, September 26, 2018

Posted by Matthew Burgess and Natasha Noy, Google AIGoogle Dataset SearchlaunchWhy is my dataset not showing up in Google Dataset Search?An Overviewadding structured metadata on their sitesschema.org/Dataset

An overview of the technology behind Google Dataset Search

Using Structured Metadata from Data Providersguidelinesscholarly discussionsprovided bypublishercreatorConnecting Replicas of Datasetsschema.org/sameAsDigital Object IdentifierReconciling to the Google Knowledge GraphKnowledge GraphcloudsvaporwaterLinking to other Google ResourcesGoogle Scholar

It provides a valuable signal about the importance and prominence of a dataset.

It gives dataset authors an easy place to see citations to their data and to get credit.

Search and Ranking of ResultsA Better Open Data Ecosystemschema.orgW3C DCATJSON-LD

Widespread adoption of open metadata formats to describe published data.

Further development of open metadata formats to describe more types of data and in more detail.

The culture of citing data the way we cite research publications, giving those who create and publish the data the credit that they deserve.

The development of tools that leverage this metadata to enable more discovery or better use of data.

So, Where is Your Dataset?Structured Data Testing TooladdDataset SearchAcknowledgementsWe would like to thank Xiaomeng Ban, Dan Brickley, Lee Butler, Thomas Chen, Corinna Cortes, Kevin Espinoza, Archana Jain, Mike Jones, Kishore Papineni, Chris Sater, Gokhan Turhan, Shubin Zhao and Andi Vajda for their work on the project and all our partners, collaborators, and early adopters for their help.

Google’s Next Generation Music Recognition

Friday, September 14, 2018

Posted by James Lyon, Google AI, ZürichNow Playingdeep neural networksSound SearchWhat’s this song?Hey Google, what’s this song?

Now Playing versus Sound SearchNow Playing miniaturized music recognitionThe Core Matching Process of Now Playing

Matching, phase 1: Finding good candidates: For every embedding, Now Playing performs a nearest neighbor search on the on-device database of songs for similar embeddings. The database uses a hybrid of spatial partitioning and vector quantization to efficiently search through millions of embedding vectors. Because the audio buffer is noisy, this search is approximate, and not every embedding will find a nearby match in the database for the correct song. However, over the whole clip, the chances of finding several nearby embeddings for the correct song are very high, so the search is narrowed to a small set of songs which got multiple hits.

Matching, phase 2: Final matching: Because the database search used above is approximate, Now Playing may not find song embeddings which are nearby to some embeddings in our query. Therefore, in order to calculate an accurate similarity score, Now Playing retrieves all embeddings for each song in the database which might be relevant to fill in the “gaps”. Then, given the sequence of embeddings from the audio buffer and another sequence of embeddings from a song in the on-device database, Now Playing estimates their similarity pairwise and adds up the estimates to get the final matching score.

Scaling up Now Playing for the Sound Search server

We quadrupled the size of the neural network used, and increased each embedding from 96 to 128 dimensions, which reduces the amount of work the neural network has to do to pack the high-dimensional input audio into a low-dimensional embedding. This is critical in improving the quality of phase two, which is very dependent on the accuracy of the raw neural network output.

We doubled the density of our embeddings — it turns out that fingerprinting audio every 0.5s instead of every 1s doesn’t reduce the quality of the individual embeddings very much, and gives us a huge boost by doubling the number of embeddings we can use for the match.

Conclusion

AcknowledgementsWe would like to thank Micha Riser, Mihajlo Velimirovic, Marvin Ritter, Ruiqi Guo, Sanjiv Kumar, Stephen Wu, Diego Melendo Casado‎, Katia Naliuka, Jason Sanders, Beat Gfeller, Julian Odell, Christian Frank, Dominik Roblek, Matt Sharifi and Blaise Aguera y Arcas‎.

Introducing the Unrestricted Adversarial Examples Challenge

Thursday, September 13, 2018

Posted by Tom B. Brown and Catherine Olsson, Research Engineers, Google Brain Teammedicinechemistryagricultureadversarial examplesprevious research on adversarial examplesimproved modelsnot subject to the “small modification” constraintconfident errors when faced with an adversaryanyUnrestricted Adversarial Examples Challenge

Adversarial examples can be generated through a variety of means, including by making small modifications to the input pixels, but also using spatial transformations, or simple guess-and-check to find misclassified inputs.

Structure of the Challengedefenderattackerfull two-sided challenge with prizes for both attacks and defenses

Is this an unambiguous picture of a bird, a bicycle, or is it ambiguous / not obvious?defender's goalattacker's goal

Examples of ambiguous and unambiguous images. Defenders must make no confident mistakes on unambiguous bird or bicycle images. We discard all images that humans find ambiguous or not obvious. All images under CC licenses 1, 2, 3, 4.

our paperHow to Participatethe project on githubleaderboardAcknowledgementsThe team behind the Unrestricted Adversarial Examples Challenge includes Tom Brown, Catherine Olsson, Nicholas Carlini, Chiyuan Zhang, and Ian Goodfellow from Google, and Paul Christiano from OpenAI.

The What-If Tool: Code-Free Probing of Machine Learning Models

Tuesday, September 11, 2018

Posted by James Wexler, Software Engineer, Google AIHow would changes to a datapoint affect my model’s prediction? Does it perform differently for various groups–for example, historically marginalized people? How diverse is the dataset I am testing my model on?Google AI PAIR initiativeWhat-If ToolTensorBoard

The What-If Tool, showing a set of 250 face pictures and their results from a model that detects smiles.

Facets

Exploring what-if scenarios on a datapoint.

CounterfactualsUCI census dataset

Comparing counterfactuals.

Analysis of Performance and Algorithmic Fairnessnumerical fairness criteriaCelebA datasetROC curveconfusion matrixequal opportunity

Comparing the performance of two slices of data on a smile detection model, with their classification thresholds set to satisfy the “equal opportunity” constraint.

Demos

Detecting misclassifications: A multiclass classification model, which predicts plant type from four measurements of a flower from the plant. The tool is helpful in showing the decision boundary of the model and what causes misclassifications. This model is trained with the UCI iris dataset.

Assessing fairness in binary classification models: The image classification model for smile detection mentioned above. The tool is helpful in assessing algorithmic fairness across different subgroups. The model was purposefully trained without providing any examples from a specific subset of the population, in order to show how the tool can help uncover such biases in models. Assessing fairness requires careful consideration of the overall context — but this is a useful quantitative starting point.

Investigating model performance across different subgroups: A regression model that predicts a subject’s age from census information. The tool is helpful in showing relative performance of the model across subgroups and how the different features individually affect the prediction. This model is trained with the UCI census dataset.

What-If in PracticeAcknowledgmentsThe What-If Tool was a collaborative effort, with UX design by Mahima Pushkarna, Facets updates by Jimbo Wilson, and input from many others. We would like to thank the Google teams that piloted the tool and provided valuable feedback and the TensorBoard team for all their help.

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