Quantum computing has rapidly advanced in both theory and practice in recent years, and with it the hope for the potential impact in real applications. One key area of interest is how quantum computers might affect machine learning. We recently demonstrated experimentally that quantum computers are able to naturally solve certain problems with complex correlations between inputs that can be incredibly hard for traditional, or “classical”, computers. This suggests that learning models made on quantum computers may be dramatically more powerful for select applications, potentially boasting faster computation, better generalization on less data, or both. Hence it is of great interest to understand in what situations such a “quantum advantage” might be achieved.
The idea of quantum advantage is typically phrased in terms of computational advantages. That is, given some task with well defined inputs and outputs, can a quantum computer achieve a more accurate result than a classical machine in a comparable runtime? There are a number of algorithms for which quantum computers are suspected to have overwhelming advantages, such as Shor’s factoring algorithm for factoring products of large primes (relevant to RSA encryption) or the quantum simulation of quantum systems. However, the difficulty of solving a problem, and hence the potential advantage for a quantum computer, can be greatly impacted by the availability of data. As such, understanding when a quantum computer can help in a machine learning task depends not only on the task, but also the data available, and a complete understanding of this must include both.
In “Power of data in quantum machine learning”, published in Nature Communications, we dissect the problem of quantum advantage in machine learning to better understand when it will apply. We show how the complexity of a problem formally changes with the availability of data, and how this sometimes has the power to elevate classical learning models to be competitive with quantum algorithms. We then develop a practical method for screening when there may be a quantum advantage for a chosen set of data embeddings in the context of kernel methods. We use the insights from the screening method and learning bounds to introduce a novel method that projects select aspects of feature maps from a quantum computer back into classical space. This enables us to imbue the quantum approach with additional insights from classical machine learning that shows the best empirical separation in quantum learning advantages to date.
Computational Power of Data The idea of quantum advantage over a classical computer is often framed in terms of computational complexity classes. Examples such as factoring large numbers and simulating quantum systems are classified as bounded quantum polynomial time (BQP) problems, which are those thought to be handled more easily by quantum computers than by classical systems. Problems easily solved on classical computers are called bounded probabilistic polynomial (BPP) problems.
We show that learning algorithms equipped with data from a quantum process, such as a natural process like fusion or chemical reactions, form a new class of problems (which we call BPP/Samp) that can efficiently perform some tasks that traditional algorithms without data cannot, and is a subclass of the problems efficiently solvable with polynomial sized advice (P/poly). This demonstrates that for some machine learning tasks, understanding the quantum advantage requires examination of available data as well.
In quantum machine learning methods, such as quantum neural networks or quantum kernel methods, a quantum program is often divided into two parts, a quantum embedding of the data (an embedding map for the feature space using a quantum computer), and the evaluation of a function applied to the data embedding. In the context of quantum computing, quantum kernel methods make use of traditional kernel methods, but use the quantum computer to evaluate part or all of the kernel on the quantum embedding, which has a different geometry than a classical embedding. It was conjectured that a quantum advantage might arise from the quantum embedding, which might be much better suited to a particular problem than any accessible classical geometry.
We developed a quick and rigorous test that can be used to quickly compare a particular quantum embedding, kernel, and data set to a range of classical kernels and assess if there is any opportunity for quantum advantage across, e.g., possible label functions such as those used for image recognition tasks. We define a geometric constant g, which quantifies the amount of data that could theoretically close that gap, based on the geometric test. This is an extremely useful technique for deciding, based on data constraints, if a quantum solution is right for the given problem.
Projected Quantum Kernel Approach One insight revealed by the geometric test, was that existing quantum kernels often suffered from a geometry that was easy to best classically because they encouraged memorization, instead of understanding. This inspired us to develop a projected quantum kernel, in which the quantum embedding is projected back to a classical representation. While this representation is still hard to compute with a classical computer directly, it comes with a number of practical advantages in comparison to staying in the quantum space entirely.
By selectly projecting back to classical space, we can retain aspects of the quantum geometry that are still hard to simulate classically, but now it is much easier to develop distance functions, and hence kernels, that are better behaved with respect to modest changes in the input than was the original quantum kernel. In addition the projected quantum kernel facilitates better integration with powerful non-linear kernels (like a squared exponential) that have been developed classically, which is much more challenging to do in the native quantum space.
This projected quantum kernel has a number of benefits over previous approaches, including an improved ability to describe non-linear functions of the existing embedding, a reduction in the resources needed to process the kernel from quadratic to linear with the number of data points, and the ability to generalize better at larger sizes. The kernel also helps to expand the geometric g, which helps to ensure the greatest potential for quantum advantage.
Data Sets Exhibit Learning Advantages The geometric test quantifies potential advantage for all possible label functions, however in practice we are most often interested in specific label functions. Using learning theoretic approaches, we also bound the generalization error for specific tasks, including those which are definitively quantum in origin. As the advantage of a quantum computer relies on its ability to use many qubits simultaneously but previous approaches scale poorly in number of qubits, it is important to verify the tasks at reasonably large qubit sizes ( > 20 ) to ensure a method has the potential to scale to real problems. For our studies we verified up to 30 qubits, which was enabled by the open source tool, TensorFlow-Quantum, enabling scaling to petaflops of compute.
Interestingly, we showed that many naturally quantum problems, even up to 30 qubits, were readily handled by classical learning methods when sufficient data were provided. Hence one conclusion is that even for some problems that look quantum, classical machine learning methods empowered by data can match the power of quantum computers. However, using the geometric construction in combination with the projected quantum kernel, we were able to construct a data set that exhibited an empirical learning advantage for a quantum model over a classical one. Thus, while it remains an open question to find such data sets in natural problems, we were able to show the existence of label functions where this can be the case. Although this problem was engineered and a quantum computational advantage would require the embeddings to be larger and more challenging, this work represents an important step in understanding the role data plays in quantum machine learning.
For this problem, we scaled up the number of qubits (n) and compared the prediction accuracy of the projected quantum kernel to existing kernel approaches and the best classical machine learning model in our dataset. Moreover, a key takeaway from these results is that although we showed the existence of datasets where a quantum computer has an advantage, for many quantum problems, classical learning methods were still the best approach. Understanding how data can affect a given problem is a key factor to consider when discussing quantum advantage in learning problems, unlike traditional computation problems for which that is not a consideration.
Conclusions When considering the ability of quantum computers to aid in machine learning, we have shown that the availability of data fundamentally changes the question. In our work, we develop a practical set of tools for examining these questions, and use them to develop a new projected quantum kernel method that has a number of advantages over existing approaches. We build towards the largest numerical demonstration to date, 30 qubits, of potential learning advantages for quantum embeddings. While a complete computational advantage on a real world application remains to be seen, this work helps set the foundation for the path forward. We encourage any interested readers to check out both the paper and related TensorFlow-Quantum tutorials that make it easy to build on this work.
Acknowledgements We would like to acknowledge our co-authors on this paper — Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, and Hartmut Neven, as well as the entirety of the Google Quantum AI team. In addition, we acknowledge valuable help and feedback from Richard Kueng, John Platt, John Preskill, Thomas Vidick, Nathan Wiebe, Chun-Ju Wu, and Balint Pato.
1Current affiliation — Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA↩
This week marks the start of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR 2021), the premier annual computer vision event consisting of the main conference, workshops and tutorials. As a leader in computer vision research and a Champion Level Sponsor, Google will have a strong presence at CVPR 2021, with over 70 publications accepted, along with the organization of and participation in multiple workshops and tutorials.
If you are participating in CVPR this year, please visit our virtual booth to learn about Google research into the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception.
You can also learn more about our research being presented at CVPR 2021 in the list below (Google affiliations in bold).
Organizing Committee Members
General Chair: Rahul Sukthankar Finance Chair: Ramin Zabih Workshop Chair: Caroline Pantofaru Area Chairs: Chen Sun, Golnaz Ghiasi, Jonathan Barron, Kostas Rematas, Negar Rostamzadeh, Noah Snavely, Sanmi Koyejo, Tsung-Yi Lin
Publications
Cross-Modal Contrastive Learning for Text-to-Image Generation (see the blog post) Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee*, Yinfei Yang
Learning Graph Embeddings for Compositional Zero-Shot Learning Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata
SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans Angela Dai, Yawar Siddiqui, Justus Thies, Julien Valentin, Matthias Nießner
3D-MAN: 3D Multi-Frame Attention Network for Object Detection Zetong Yang*, Yin Zhou, Zhifeng Chen, Jiquan Ngiam
MIST: Multiple Instance Spatial Transformer Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi
OCONet: Image Extrapolation by Object Completion Richard Strong Bowen*, Huiwen Chang, Charles Herrmann*, Piotr Teterwak*, Ce Liu, Ramin Zabih
Ranking Neural Checkpoints Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong
LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces From Video Using Pose and Lighting Normalization Avisek Lahiri, Vivek Kwatra, Christian Frueh, John Lewis, Chris Bregler
Differentiable Patch Selection for Image Recognition Jean-Baptiste Cordonnier*, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences Feitong Tan, Danhang Tang, Mingsong Dou, Kaiwen Guo, Rohit Pandey, Cem Keskin, Ruofei Du, Deqing Sun, Sofien Bouaziz, Sean Fanello, Ping Tan, Yinda Zhang
VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation (see the blog post) Siyuan Qiao*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov
Wide-Baseline Relative Camera Pose Estimation With Directional Learning Kefan Chen, Noah Snavely, Ameesh Makadia
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking Jennifer Jang, Heinrich Jiang
Repopulating Street Scenes Yifan Wang*, Andrew Liu, Richard Tucker, Jiajun Wu, Brian L. Curless, Steven M. Seitz, Noah Snavely
MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers (see the blog post) Huiyu Wang*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
IBRNet: Learning Multi-View Image-Based Rendering Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser
From Points to Multi-Object 3D Reconstruction Francis Engelmann*, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari
Learning Compositional Representation for 4D Captures With Neural ODE Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise Andrei Kapishnikov, Subhashini Venugopalan, Besim Avci, Ben Wedin, Michael Terry, Tolga Bolukbasi
De-Rendering the World’s Revolutionary Artefacts Shangzhe Wu*, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa
Spatiotemporal Contrastive Video Representation Learning Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui
Decoupled Dynamic Filter Networks Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang
NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering Using RGB Cameras Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Kaiwen Guo, Minye Wu, Lan Xu
Regularizing Generative Adversarial Networks Under Limited Data Hung-Yu Tseng*, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang
SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, Federico Tombari
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron
Adversarially Adaptive Normalization for Single Domain Generalization Xinjie Fan*, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, Mingyuan Zhou
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim
Adversarial Robustness Across Representation Spaces Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan
Background Splitting: Finding Rare Classes in a Sea of Background Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian
Searching for Fast Model Families on Datacenter Accelerators Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, Norman P. Jouppi
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations (see the blog post) Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Matthias Grundmann
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food Quin Thames, Arjun Karpur, Wade Norris, Fangting Xia, Liviu Panait, Tobias Weyand, Jack Sim
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning Chen Wei*, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang
DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution Siyuan Qiao, Liang-Chieh Chen, Alan Yuille
DeRF: Decomposed Radiance Fields Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi
Variational Transformer Networks for Layout Generation (see the blog post) Diego Martin Arroyo, Janis Postels, Federico Tombari
Rich Features for Perceptual Quality Assessment of UGC Videos Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds Li Yi, Boqing Gong, Thomas Funkhouser
Neural Descent for Visual 3D Human Pose and Shape Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji
Look Before You Speak: Visually Contextualized Utterances Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid
LASR: Learning Articulated Shape Reconstruction From a Monocular Video Gengshan Yang*, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu
MoViNets: Mobile Video Networks for Efficient Video Recognition Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong
No Shadow Left Behind: Removing Objects and Their Shadows Using Approximate Lighting and Geometry Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian Curless
On Robustness and Transferability of Convolutional Neural Networks Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
Robust and Accurate Object Detection via Adversarial Learning Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong
To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan, Xiao Zhang, Dragomir Anguelov
Bottleneck Transformers for Visual Recognition Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani
Faster Meta Update Strategy for Noise-Robust Deep Learning Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang
Correlated Input-Dependent Label Noise in Large-Scale Image Classification Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent
Learned Initializations for Optimizing Coordinate-Based Neural Representations Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng
Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi, Yin Cui, Aravind Srinivas*, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph
Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, Dragomir Anguelov
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments Siyan Dong, Qingnan Fan, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas Guibas
Taskology: Utilizing Task Relations at Scale Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad*, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon
Omnimatte: Associating Objects and Their Effects in Video Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein
AutoFlow: Learning a Better Training Set for Optical Flow Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, and Ce Liu
Unsupervised Multi-Source Domain Adaptation Without Access to Source Data Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury
Meta Pseudo Labels Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le
Spatially-Varying Outdoor Lighting Estimation From Intrinsics Yongjie Zhu, Yinda Zhang, Si Li, Boxin Shi
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization Long Zhao*, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu
Benchmarking Representation Learning for Natural World Image Collections Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, Oisin Mac Aodha
Scaling Local Self-Attention for Parameter Efficient Visual Backbones Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control Tomas Jakab*, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz
POSEFusion: Pose-Guided Selective Fusion for Single-View Human Volumetric Capture Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu
Workshops (only Google affiliations are noted)
Media Forensics Organizers: Christoph Bregler
Safe Artificial Intelligence for Automated Driving Invited Speakers: Been Kim
VizWiz Grand Challenge Organizers: Meredith Morris
3D Vision and Robotics Invited Speaker: Andy Zeng
New Trends in Image Restoration and Enhancement Workshop and Challenges on Image and Video Processing Organizers: Ming-Hsuan Yang Program Committee: George Toderici, Ming-Hsuan Yang
2nd Workshop on Extreme Vision Modeling Invited Speakers: Quoc Le, Chen Sun
First International Workshop on Affective Understanding in Video Organizers: Gautam Prasad, Ting Liu
Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges Program Committee: Nicholas Carlini, Nicolas Papernot
Ethical Considerations in Creative Applications of Computer Vision Invited Speaker: Alex Hanna Organizers: Negar Rostamzadeh, Emily Denton, Linda Petrini
Visual Question Answering Workshop Invited Speaker: Vittorio Ferrari
Sixth International Skin Imaging Collaboration (ISIC) Workshop on Skin Image Analysis Invited Speakers: Sandra Avila Organizers: Yuan Liu Steering Committee: Yuan Liu, Dale Webster
The 4th Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in Conjunction with IEEE CVPR 2021 Invited Speakers: Peyman Milanfar, Chelsea Finn
The 3rd CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics Invited Speaker: Andrea Tagliasacchi
Robust Video Scene Understanding: Tracking and Video Segmentation Organizers: Jordi Pont-Tuset, Sergi Caelles, Jack Valmadre, Alex Bewley
4th Workshop and Challenge on Learned Image Compression Invited Speaker: Rianne van den Berg Organizers: George Toderici, Lucas Theis, Johannes Ballé, Eirikur Agustsson, Nick Johnston, Fabian Mentzer
The Third Workshop on Precognition: Seeing Through the Future Invited Speaker: Anelia AngelovaOrganizers: Utsav Prabhu Program Committee: Chen Sun, David Ross
Computational Cameras and Displays Organizers: Tali Dekel Keynote Talks: Paul Debevec Program Committee: Ayan Chakrabarti, Tali Dekel
2nd Embodied AI Workshop Organizing Committee: Anthony Francis Challenge Organizers: Peter Anderson, Anthony Francis, Alex Ku, Alexander Toshev Scientific Advisory Board: Alexander Toshev
Responsible Computer Vision Program Committee: Caroline Pantofaru, Utsav Prabhu, Susanna Ricco, Negar Rostamzadeh, Candice Schumann
Dynamic Neural Networks Meets Computer Vision Invited Speaker: Azalia Mirhoseini
Interactive Workshop on Bridging the Gap between Subjective and Computational Measurements of Machine Creativity Invited Speaker: David Bau
GAZE 2021: The 3rd International Workshop on Gaze Estimation and Prediction in the Wild Organizer: Thabo Beeler Program Committee: Thabo Beeler
Sight and Sound Organizers: William Freeman
Future of Computer Vision Datasets Invited Speaker: Emily Denton, Caroline Pantofaru
Open World Vision Invited Speakers: Rahul Sukthankar
The 3rd Workshop on Learning from Unlabeled Videos Organizers: Anelia Angelova, Honglak Lee Program Committee: AJ Piergiovanni
4th International Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues — With a Focus on Mobile Platform Applications Organizers: Anelia Angelova
4th Workshop on Efficient Deep Learning for Computer Vision Invited Speaker: Andrew HowardOrganizers: Pete Warden, Andrew Howard
Second International Workshop on Large Scale Holistic Video Understanding Invited Speaker: Cordelia Schmid Program Committee: AJ Piergiovanni Organizers: David Ross
Neural Architecture Search 1st Lightweight NAS Challenge and Moving Beyond Invited Speakers: Sara Sabour
The Second Workshop on Fair, Data-Efficient, and Trusted Computer Vision Invited Speakers: Gaurav Aggarwal
The 17th Embedded Vision Workshop General Chair: Anelia Angelova
8th Workshop on Fine-Grained Visual Categorization Organizers: Christine Kaeser-Chen, Kimberly Wilber
AI for Content Creation Invited Speaker: Tali Dekel, Jon Barron, Emily Denton Organizers: Deqing Sun
Frontiers of Monocular 3D Perception Invited Speakers: Anelia Angelova, Cordelia Schmid, Noah Snavely
Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision Organizers: Emily Denton
The 1st Workshop on Future Video Conferencing Invited Speakers: Chuo-Ling Chang, Sergi Caelles
Tutorials (only Google affiliations are noted)
Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision Organizer: Emily Denton
Data-Efficient Learning in An Imperfect World Organizers: Boqing Gong, Ting Chen
Semantic Segmentation of Point Clouds: a Deep Learning Framework for Cultural Heritage Invited Speaker: Manzil Zaheer
From VQA to VLN: Recent Advances in Vision-and-Language Research Organizer: Peter Anderson * Indicates work done while at Google
Simulation empowers various engineering disciplines to quickly prototype with minimal human effort. In robotics, physics simulations provide a safe and inexpensive virtual playground for robots to acquire physical skills with techniques such as deep reinforcement learning (DRL). However, as the hand-derived physics in simulations does not match the real world exactly, control policies trained entirely within simulation can fail when tested on real hardware — a challenge known as the sim-to-real gap or the domain adaptation problem. The sim-to-real gap for perception-based tasks (such as grasping) has been tackled using RL-CycleGAN and RetinaGAN, but there is still a gap caused by the dynamics of robotic systems. This prompts us to ask, can we learn a more accurate physics simulator from a handful of real robot trajectories? If so, such an improved simulator could be used to refine the robot controller using standard DRL training, so that it succeeds in the real world.
In our ICRA 2021 publication “SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning”, we propose to treat the physics simulator as a learnable component that is trained by DRL with a special reward function that penalizes discrepancies between the trajectories (i.e., the movement of the robots over time) generated in simulation and a small number of trajectories that are collected on real robots. We use generative adversarial networks (GANs) to provide such a reward, and formulate a hybrid simulator that combines learnable neural networks and analytical physics equations, to balance model expressiveness and physical correctness. On robotic locomotion tasks, our method outperforms multiple strong baselines, including domain randomization.
A Learnable Hybrid Simulator A traditional physics simulator is a program that solves differential equations to simulate the movement or interactions of objects in a virtual world. For this work, it is necessary to build different physical models to represent different environments – if a robot walks on a mattress, the deformation of the mattress needs to be taken into account (e.g., with the finite element method). However, due to the diversity of the scenarios that robots could encounter in the real world, it would be tedious (or even impossible) for such environment-specific modeling techniques, which is why it is useful to instead take an approach based on machine learning. Although simulators can be learned entirely from data, if the training data does not include a wide enough variety of situations, the learned simulator might violate the laws of physics (i.e., deviate from the real-world dynamics) if it needs to simulate situations for which it was not trained. As a result, the robot that is trained in such a limited simulator is more likely to fail in the real world.
To overcome this complication, we construct a hybrid simulator that combines both learnable neural networks and physics equations. Specifically, we replace what are often manually-defined simulator parameters — contact parameters (e.g., friction and restitution coefficients) and motor parameters (e.g., motor gains) — with a learnable simulation parameter function because the unmodeled details of contact and motor dynamics are major causes of the sim-to-real gap. Unlike conventional simulators in which these parameters are treated as constants, in the hybrid simulator they are state-dependent — they can change according to the state of the robot. For example, motors can become weaker at higher speed. These typically unmodeled physical phenomena can be captured using the state-dependent simulation parameter functions. Moreover, while contact and motor parameters are usually difficult to identify and subject to change due to wear-and-tear, our hybrid simulator can learn them automatically from data. For example, rather than having to manually specify the parameters of a robot’s foot against every possible surface it might contact, the simulation learns these parameters from training data.
The other part of the hybrid simulator is made up of physics equations that ensure the simulation obeys fundamental laws of physics, such as conservation of energy, making it a closer approximation to the real world and thus reducing the sim-to-real gap.
In our earlier mattress example, the learnable hybrid simulator is able to mimic the contact forces from the mattress. Because the learned contact parameters are state-dependent, the simulator can modulate contact forces based on the distance and velocity of the robot’s feet relative to the mattress, mimicking the effect of the stiffness and damping of a deformable surface. As a result, we do not need to analytically devise a model specifically for deformable surfaces.
Using GANs for Simulator Learning Successfully learning the simulation parameter functions discussed above would result in a hybrid simulator that can generate similar trajectories to the ones collected on the real robot. The key that enables this learning is defining a metric for the similarity between trajectories. GANs, initially designed to generate synthetic images that share the same distribution, or “style,” with a small number of real images, can be used to generate synthetic trajectories that are indistinguishable from real ones. GANs have two main parts, a generator that learns to generate new instances, and a discriminator that evaluates how similar the new instances are to the training data. In this case, the learnable hybrid simulator serves as the GAN generator, while the GAN discriminator provides the similarity scores.
Fitting parameters of simulation models to data collected in the real world, a process called system identification (SysID), has been a common practice in many engineering fields. For example, the stiffness parameter of a deformable surface can be identified by measuring the displacements of the surface under different pressures. This process is typically manual and tedious, but using GANs can be much more efficient. For example, SysID often requires a hand-crafted metric for the discrepancy between simulated and real trajectories. With GANs, such a metric is automatically learned by the discriminator. Furthermore, to calculate the discrepancy metric, conventional SysID requires pairing each simulated trajectory to a corresponding real-world one that is generated using the same control policy. Since the GAN discriminator takes only one trajectory as the input and calculates the likelihood that it is collected in the real world, this one-to-one pairing is not needed.
Using Reinforcement Learning (RL) to Learn the Simulator and Refine the Policy Putting everything together, we formulate simulation learning as an RL problem. A neural network learns the state-dependent contact and motor parameters from a small number of real-world trajectories. The neural network is optimized to minimize the error between the simulated and the real trajectories. Note that it is important to minimize this error over an extended period of time — a simulation that accurately predicts a more distant future will lead to a better control policy. RL is well suited to this because it optimizes the accumulated reward over time, rather than just optimizing a single-step reward.
After the hybrid simulator is learned and becomes more accurate, we use RL again to refine the robot’s control policy within the simulation (e.g., walking across a surface, shown below).
Evaluation Due to limited access to real robots during 2020, we created a second and different simulation (target domain) as a proxy of the real-world. The change of dynamics between the source and the target domains are large enough to approximate different sim-to-real gaps (e.g., making one leg heavier, walking on deformable surfaces instead of hard floor). We assessed whether our hybrid simulator, with no knowledge of these changes, could learn to match the dynamics in the target domain, and if the refined policy in this learned simulator could be successfully deployed in the target domain.
Qualitative results below show that simulation learning with less than 10 minutes of data collected in the target domain (where the floor is deformable) is able to generate a refined policy that performs much better for two robots with different morphologies and dynamics.
Quantitative results below show that SimGAN outperforms multiple state-of-the-art baselines, including domain randomization (DR) and direct finetuning in target domains (FT).
Conclusion The sim-to-real gap is one of the key bottlenecks that prevents robots from tapping into the power of reinforcement learning. We tackle this challenge by learning a simulator that can more faithfully model real-world dynamics, while using only a small amount of real-world data. The control policy that is refined in this simulator can be successfully deployed. To achieve this, we augment a classical physics simulator with learnable components, and train this hybrid simulator using adversarial reinforcement learning. To date we have tested its application to locomotion tasks, we hope to build on this general framework by applying it to other robot learning tasks, such as navigation and manipulation.
Acknowledgements We'd like to thank our paper co-authors: Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, and Sergey Levine. We would also like to thank the team members of Robotics at Google for discussions and feedback.
In 2016, we introduced Open Images, a collaborative release of ~9 million images annotated with image labels spanning thousands of object categories and bounding box annotations for 600 classes. Since then, we have made several updates, including the release of crowdsourced data to the Open Images Extended collection to improve diversity of object annotations. While the labels provided with these datasets were expansive, they did not focus on sensitive attributes for people, which are critically important for many machine learning (ML) fairness tasks, such as fairness evaluations and bias mitigation. In fact, finding datasets that include thorough labeling of such sensitive attributes is difficult, particularly in the domain of computer vision.
Today, we introduce the More Inclusive Annotations for People (MIAP) dataset in the Open Images Extended collection. The collection contains more complete bounding box annotations for the person class hierarchy in 100k images containing people. Each annotation is also labeled with fairness-related attributes, including perceived gender presentation and perceived age range. With the increasing focus on reducing unfair bias as part of responsible AI research, we hope these annotations will encourage researchers already leveraging Open Images to incorporate fairness analysis in their research.
Annotations in Open Images Each image in the original Open Images dataset contains image-level annotations that broadly describe the image and bounding boxes drawn around specific objects. To avoid drawing multiple boxes around the same object, less specific classes were temporarily pruned from the label candidate set, a process that we refer to as hierarchical de-duplication. For example, an image with labels animal, cat, and washing machine has bounding boxes annotated for cat and washing machine, but not for the redundant class animal.
The MIAP dataset addresses the five classes that are part of the person hierarchy in the original Open Images dataset: person, man, woman, boy, girl. The existence of these labels make the Open Images dataset uniquely valuable for research advancing responsible AI, allowing one to train a general person detector with access to gender- and age-range-specific labels for fairness analysis and bias mitigation.
However, we found that the combination of hierarchical de-duplication and societally imposed distinctions between woman/girl and man/boy introduced limitations in the original annotations. For example, if annotators were asked to draw boxes for the class girl, they would not draw a box around a boy in the image. They may or may not draw a box around a woman depending on their assessment of the age of the individual and their cultural understanding of the concept of girl. These decisions could be applied inconsistently between images, depending on the cultural background of the individual annotator, the appearance of an individual, and the context of the scene. Consequently, the bounding box annotations in some images were incomplete, with some people who appeared prominently not being annotated.
Annotations in MIAP The new MIAP annotations are designed to address these limitations and fulfill the promise of Open Images as a dataset that will enable new advances in machine learning fairness research. Rather than asking annotators to draw boxes for the most specific class from the hierarchy (e.g., girl), we invert the procedure, always requesting bounding boxes for the gender- and age-agnostic person class. All person boxes are then separately associated with labels for perceived gender presentation (predominantly feminine, predominantly masculine, or unknown) and age presentation (young, middle, older, or unknown). We recognize that gender is not binary and that an individual's gender identity may not match their perceived or intended gender presentation and, in an effort to mitigate the effects of unconscious bias on the annotations, we reminded annotators that norms around gender expression vary across cultures and have changed over time.
This procedure adds a significant number of boxes that were previously missing.
Over the 100k images that include people, the number of person bounding boxes have increased from ~358k to ~454k. The number of bounding boxes per perceived gender presentation and perceived age presentation increased consistently. These new annotations provide more complete ground truth for training a person detector as well as more accurate subgroup labels for incorporating fairness into computer vision research.
Intended Use We include annotations for perceived age range and gender presentation for person bounding boxes because we believe these annotations are necessary to advance the ability to better understand and work to mitigate and eliminate unfair bias or disparate performance across protected subgroups within the field of image understanding. We note that the labels capture the gender and age range presentation as assessed by a third party based on visual cues alone, rather than an individual's self-identified gender or actual age. We do not support or condone building or deploying gender and/or age presentation classifiers trained from these annotations as we believe the risks associated with the use of these technologies outside fairness research outweigh any potential benefits.
Acknowledgements The core team behind this work included Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru. We would also like to thank Alex Hanna, Reena Jana, Alina Kuznetsova, Matteo Malloci, Stefano Pellegrini, Jordi Pont-Tuset, and Mahima Pushkarna, for their contributions to the project.
Disciplines in the natural sciences, social sciences, and medicine all have to grapple with how to evaluate and compare results within the context of the continually changing real world. In contrast, a significant body of machine learning (ML) research uses a different method that relies on the assumption of a fixed world: measure the performance of a baseline model on fixed data sets, then build a new model aimed at improving on the baseline, and evaluate its performance (on the same fixed data) by comparing its performance to the baseline.
Research into robotics systems and their applications to the real world requires a rethinking of this experiment design. Even in controlled robotic lab environments, it is possible that real-world changes cause the baseline model to perform inconsistently over time, making it unclear whether new models’ performance is an improvement compared to the baseline, or just the result of unintentional, random changes in the experiment setup. As robotics research advances into more complex and challenging real-world scenarios, there is a growing need for both understanding the impact of the ever-changing world on baselines and developing systematic methods to generate informative and clear results.
In this post, we demonstrate how robotics research, even in the relatively controlled environment of a lab, is meaningfully affected by changes in the environment, and discuss how to address this fundamental challenge using random assignment and A/B testing. Although these are classical research methods, they are not generally employed by default in robotics research — yet, they are critical to producing meaningful and measurable scientific results for robotics in real-world scenarios. Additionally, we cover the costs, benefits, and other considerations of using these methods.
The Ever-Changing Real World in Robotics Even in a robotics lab environment, which is designed to minimize all changes that are not experimental conditions, it is notoriously difficult to set up a perfectly reproducible experiment. Robots get bumped and are subject to wear and tear, lighting changes affect perception, battery charge influences the torque applied to motors — all things that can affect results in ways large and small.
To illustrate this on real robot data, we collected success rate data on one of our simplest setups — moving identical foam dice from one bin to another. For this task, we ran about 33k task trials on two robots over more than five months with the same software and ML model, and took the overall success rate of the last two weeks as baseline. We then measured the historic performance over time in this “very well controlled” environment.
Given that we did not purposefully change anything during data collection, one would expect the success rate to be statistically similar over time. And yet, this is not what was observed.
Using the sequential data from the plot above, one might conclude that the model ran during weeks 13-14 performed best and that ran during weeks 9-10 performed the worst. One might also expect most, if not all, of the confidence intervals above to contain 0, but only one did. Because no changes were made at any time during these trials, this example effectively demonstrates the impact of unintentional, random real-world changes on even very simple setups. It’s also worth noting that having more trials per experiment wouldn’t remove these differences, instead they will more likely produce a narrower confidence interval making the impact more obvious.
However, what happens when one uses random assignment to compare results, grouping the data randomly rather than sequentially? To answer this, we randomly assigned the above data to the same number of groups for comparison with the baseline. This is equivalent to performing A/B testing where all groups receive the same treatment.
Looking at the chart, we observe that the confidence intervals include zero, indicating success similar to the baseline, as expected.
We performed similar studies with a few other robotics tasks, comparing between sequential and random assignments. They all yielded similar results.
We see that even with no intentional changes, there are statistically significant differences observed for sequential assignment, while random assignment shows the expected result of no statistically significant differences.
Considerations for A/B testing in robotics While it’s clear based on the above that A/B testing with random assignment is an effective way to control for the unexplainable variance of the real world in robotics, there are some considerations when adopting this approach. Here are several, along with their accompanying pros, cons, and solutions:
The Path Forward The A/B testing experiment framework has been successfully used for a long time in many scientific disciplines to measure performance against changing, unpredictable real-world environments. In this blog post, we show that robotics research can benefit from using this same methodology: it improves the quality and confidence of research results, and avoids the impossible task of perfectly controlling all elements of a fundamentally changing environment. Doing this well requires infrastructure to continuously operate robots, collect data, and tools to make the statistical framework easily accessible to researchers.
Acknowledgements Arnab Bose, Tuna Toksoz, Yuheng Kuang, Anthony Brohan, Razvan Sudulescu developed the experiment infrastructure and conducted the research. Matthieu Devin suggested the A/A analysis to showcase the differences using existing data. Special thanks to Bill Heavlin, Chris Harris, Vincent Vanhoucke who provided invaluable feedback and support to the work.
Representation learning is a machine learning (ML) method that trains a model to identify salient features that can be applied to a variety of downstream tasks, ranging from natural language processing (e.g., BERT and ALBERT) to image analysis and classification (e.g., Inception layers and SimCLR). Last year, we introduced a benchmark for comparing speech representations and a new, generally-useful speech representation model (TRILL). TRILL is based on temporal proximity, and tries to map speech that occurs close together in time to a lower-dimensional embedding that captures temporal proximity in the embedding space. Since its release, the research community has used TRILL on a diverse set of tasks, such as age classification, video thumbnail selection, and language identification. However, despite achieving state-of-the-art performance, TRILL and other neural network-based approaches require more memory and take longer to compute than signal processing operations that deal with simple features, like loudness, average energy, pitch, etc.
In our recent paper "FRILL: A Non-Semantic Speech Embedding for Mobile Devices", to appear at Interspeech 2021, we create a new model that is 40% the size of TRILL and and a feature set that can be computed over 32x faster on mobile phone, with an average decrease in accuracy of less than 2%. This marks an important step towards fully on-device applications of speech ML models, which will lead to better personalization, improved user experiences and greater privacy, an important aspect of developing AI responsibly. We release the code to create FRILL on github, and a pre-trained FRILL model on TensorFlow Hub.
FRILL: Smaller, Faster TRILL The TRILL architecture is based on a modified version of ResNet50, an architecture that is computationally taxing for constrained hardware, like mobile phones or smart home devices. On the other hand, architectures like MobileNetV3 have been designed with hardware-aware AutoML to perform well on mobile devices. To take advantage of this, we leverage knowledge distillation to combine the benefits of MobileNetV3’s performance with TRILL’s representations.
In the distillation process, the smaller model (i.e., the "student") tries to match the output of the larger model ("teacher") on the AudioSet dataset. Whereas the original TRILL model learned its weights by optimizing a self-supervised loss that clustered audio segments close in time, the student model learns its weights through a fully-supervised loss that ignores temporal matching and instead tries to match TRILL outputs on the training data. The fully-supervised learning signal is often stronger than self-supervision, and allows us to train more quickly.
Choosing the Best Student Model We perform distillation with a variety of student models, each trained with a specific combination of architecture choices (explained below). To measure each student model’s latency, we leverage TensorFlow Lite (TFLite), a framework that enables execution of TensorFlow models on edge devices. Each candidate model is first converted into TFLite’s flatbuffer format for 32-bit floating point inference and then sent to the target device (in this case, a Pixel 1) for benchmarking. These measurements help us to accurately assess the latency versus quality tradeoffs across all student models and to minimize the loss of quality in the conversion process.
Architecture Choices and Optimizations We explored different neural network architectures and features that balance latency and accuracy — models with fewer parameters are usually smaller and faster, but have less representational power and therefore generate less generally-useful representations. We trained 144 different models across a number of hyperparameters, all based on the MobileNetV3 architecture:
Results We evaluated each of these models on the Non-Semantic Speech Benchmark (NOSS) and two new tasks — a challenging task to detect whether a speaker is wearing a mask and the human-noise subset of the Environment Sound Classification dataset, which includes labels like “coughing” and “sneezing”. After eliminating models that have strictly better alternatives, we are left with eight ”frontier” models on the quality vs. latency curve, which are the models that had no faster and better performance alternatives at a corresponding quality threshold or latency in our batch of 144 models. We plot the latency vs. quality curve of only these "frontier" models below, and we ignore models that are strictly worse.
FRILL is the best performing sub-10ms inference model, with an inference time of 8.5 ms on a Pixel 1 (about 32x faster than TRILL), and is also roughly 40% the size of TRILL. The frontier curve plateaus at about 10ms latency, which means that at low latency, one can achieve much better performance with minimal latency costs, while achieving improved performance at latencies beyond 10ms is more difficult. This supports our choice of experiment hyperparameters. FRILL's per-task performance is shown in the table below.
Finally, we evaluate the relative contribution of each of our hyperparameters. We find that for our experiments, quantization-aware training, bottleneck compression and global average pooling most reduced the latency of the resulting models. At the same time bottleneck compression most reduced the quality of the resulting model, while pooling reduced the model performance the least. The architecture width parameter was an important factor in reducing the model size, with minimal performance degradation.
Our work is an important step in bringing the full benefits of speech machine learning research to mobile devices. We also provide our public model, corresponding model card, and evaluation code to help the research community responsibly develop even more applications for on-device speech representation research.
Acknowledgements We'd like to thank our paper co-authors: Jacob Peplinski, Jake Garrison and Shwetak Patel. We'd like to thank Aren Jansen for his technical support on this project, Françoise Beaufays, and Tulsee Doshi for help open sourcing the model, and Google Research, Tokyo for logistical support.
