Google AI Blog

How Can Neural Network Similarity Help Us Understand Training and Generalization?

Thursday, June 21, 2018

Posted by Maithra Raghu, Google Brain Team and Ari S. Morcos, DeepMind
previous postCanonical Correlation Analysisconvolutional neural networksInsights on Representational Similarity in Neural Networks with Canonical Correlationrecurrent neural networkscode used for applying CCA on neural networksRepresentational Similarity of Memorizing and Generalizing CNNs

generalizing networks: CNNs trained on data with unmodified, accurate labels and which learn solutions which generalize to novel data.

memorizing networks: CNNs trained on datasets with randomized labels such that they must memorize the training data and cannot, by definition, generalize (as in Zhang et al., 2017).

our paperdifferentsoftmax

Groups of generalizing networks (blue) converge to more similar solutions than groups of memorizing networks (red). CCA distance was calculated between groups of networks trained on real CIFAR-10 labels (“Generalizing”) or randomized CIFAR-10 labels (“Memorizing”) and between pairs of memorizing and generalizing networks (“Inter”).

while there are many different ways to memorize the training data (resulting in greater CCA distances), there are fewer ways to learn generalizable solutionsUnderstanding the Training Dynamics of Recurrent Neural Networksbottom-upprevious work

Convergence dynamics for RNNs over the course of training exhibit bottom up convergence, as layers closer to the input converge to their final representations earlier in training than later layers. For example, layer 1 converges to its final representation earlier in training than layer 2 than layer 3 and so on. Epoch designates the number of times the model has seen the entire training set while different colors represent the convergence dynamics of different layers.

ConclusionscodeAcknowledgementsSpecial thanks to Samy Bengio, who is a co-author on this work. We also thank Martin Wattenberg, Jascha Sohl-Dickstein and Jon Kleinberg for helpful comments.

Google at CVPR 2018

Monday, June 18, 2018

2018 Conference on Computer Vision and Pattern Recognitionworkshops tutorialsmachine perceptionportrait mode on the Pixel 2 and Pixel 2 XL smartphonesOpen Images V4 datasetblueOrganization Ramin Zabih Sameer Agarwal, Aseem Agrawala, Jon Barron, Abhinav Shrivastava, Carl Vondrick, Ming-Hsuan YangOrals/SpotlightsUnsupervised Discovery of Object Landmarks as Structural Representations Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak LeeDoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor Tao Yu, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Qionghai Dai, Hao Li, Gerard Pons-Moll, Yebin Liu Neural Kinematic Networks for Unsupervised Motion Retargetting Ruben Villegas, Jimei Yang, Duygu Ceylan, Honglak Lee Burst Denoising with Kernel Prediction Networks Ben Mildenhall, Jiawen Chen, Jonathan Barron, Robert Carroll, Dillon Sharlet, Ren NgQuantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob, Skirmantas Kligys, Bo Chen, Matthew Tang, Menglong Zhu, Andrew Howard, Dmitry Kalenichenko, Hartwig AdamAVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions Chunhui Gu, Chen Sun, David Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra Malik Focal Visual-Text Attention for Visual Question Answering Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander G. HauptmannInferring Light Fields from Shadows Manel Baradad, Vickie Ye, Adam Yedida, Fredo Durand, William Freeman, Gregory Wornell, Antonio Torralba Modifying Non-Local Variations Across Multiple Views Tal Tlusty, Tomer Michaeli, Tali Dekel, Lihi Zelnik-ManorIterative Visual Reasoning Beyond Convolutions Xinlei Chen, Li-jia Li, Fei-Fei Li, Abhinav Gupta Unsupervised Training for 3D Morphable Model RegressionKyle Genova, Forrester Cole, Aaron Maschinot, Daniel Vlasic, Aaron Sarna, William FreemanLearning Transferable Architectures for Scalable Image RecognitionBarret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le The iNaturalist Species Classification and Detection Dataset Grant van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie Learning Intrinsic Image Decomposition from Watching the World Zhengqi Li, Noah Snavely Learning Intelligent Dialogs for Bounding Box Annotation Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert, Vittorio Ferrari PostersRevisiting Knowledge Transfer for Training Object Class Detectors Jasper Uijlings, Stefan Popov, Vittorio Ferrari Rethinking the Faster R-CNN Architecture for Temporal Action Localization Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold, David Ross, Jia Deng, Rahul Sukthankar Hierarchical Novelty Detection for Visual Object Recognition Kibok Lee, Kimin Lee, Kyle Min, Yuting Zhang, Jinwoo Shin, Honglak Lee COCO-Stuff: Thing and Stuff Classes in Context Holger Caesar, Jasper Uijlings, Vittorio Ferrari Appearance-and-Relation Networks for Video Classification Limin Wang, Wei Li, Wen Li, Luc Van Gool MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep NetworksAriel Gordon, Elad Eban, Bo Chen, Ofir Nachum, Tien-Ju Yang, Edward Choi Deformable Shape Completion with Graph Convolutional AutoencodersOr Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia MegaDepth: Learning Single-View Depth Prediction from Internet Photos Zhengqi Li, Noah Snavely Unsupervised Discovery of Object Landmarks as Structural Representations Yuting Zhang, Yijie Guo, Yixin Jin, Yijun Luo, Zhiyuan He, Honglak Lee Burst Denoising with Kernel Prediction Networks Ben Mildenhall, Jiawen Chen, Jonathan Barron, Robert Carroll, Dillon Sharlet, Ren Ng Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference Benoit Jacob, Skirmantas Kligys, Bo Chen, Matthew Tang, Menglong Zhu, Andrew Howard, Dmitry Kalenichenko, Hartwig AdamPix3D: Dataset and Methods for Single-Image 3D Shape ModelingXingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Tianfan Xue, Joshua Tenenbaum, William FreemanSparse, Smart Contours to Represent and Edit Images Tali Dekel, Dilip Krishnan, Chuang Gan, Ce Liu, William FreemanMaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features Liang-Chieh Chen, Alexander Hermans, George Papandreou, Florian Schroff, Peng Wang, Hartwig Adam Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Yin Cui, Yang Song, Chen Sun, Andrew Howard, Serge Belongie Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Sung Jin Hwang, George Toderici, Troy Chinen, Joel Shor MobileNetV2: Inverted Residuals and Linear BottlenecksMark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Juergen Sturm, Matthias NießnerSim2Real View Invariant Visual Servoing by Recurrent Control Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey LevineAlternating-Stereo VINS: Observability Analysis and Performance Evaluation Mrinal Kanti Paul, Stergios Roumeliotis Soccer on Your Tabletop Konstantinos Rematas, Ira Kemelmacher, Brian Curless, Steve Seitz Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints Reza Mahjourian, Martin Wicke, Anelia Angelova AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions Chunhui Gu, Chen Sun, David Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, Jitendra MalikInferring Light Fields from Shadows Manel Baradad, Vickie Ye, Adam Yedida, Fredo Durand, William Freeman, Gregory Wornell, Antonio Torralba Modifying Non-Local Variations Across Multiple Views Tal Tlusty, Tomer Michaeli, Tali Dekel, Lihi Zelnik-Manor Aperture Supervision for Monocular Depth Estimation Pratul Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, Jonathan BarronInstance Embedding Transfer to Unsupervised Video Object SegmentationSiyang Li, Bryan Seybold, Alexey Vorobyov, Alireza Fathi, Qin Huang, C.-C. Jay Kuo Frame-Recurrent Video Super-Resolution Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew BrownWeakly Supervised Action Localization by Sparse Temporal Pooling Network Phuc Nguyen, Ting Liu, Gautam Prasad, Bohyung Han Iterative Visual Reasoning Beyond Convolutions Xinlei Chen, Li-jia Li, Fei-Fei Li, Abhinav Gupta Learning and Using the Arrow of Time Donglai Wei, Andrew Zisserman, William Freeman, Joseph Lim HydraNets: Specialized Dynamic Architectures for Efficient Inference Ravi Teja Mullapudi, Noam Shazeer, William Mark, Kayvon Fatahalian Thoracic Disease Identification and Localization with Limited Supervision Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-jia Li, Fei-Fei Li Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak LeeDeep Semantic Face Deblurring Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan YangUnsupervised Training for 3D Morphable Model Regression Kyle Genova, Forrester Cole, Aaron Maschinot, Daniel Vlasic, Aaron Sarna, William FreemanLearning Transferable Architectures for Scalable Image Recognition Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le Learning Intrinsic Image Decomposition from Watching the World Zhengqi Li, Noah SnavelyPiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection Nian Liu, Junwei Han, Ming-Hsuan YangTutorials Computer Vision for Robotics and Driving Anelia Angelova, Sanja Fidler Unsupervised Visual Learning Pierre Sermanet, Anelia Angelova UltraFast 3D Sensing, Reconstruction and Understanding of People, Objects and EnvironmentsSean Fanello, Julien Valentin, Jonathan Taylor, Christoph Rhemann, Adarsh Kowdle, Jürgen Sturm, Christine Kaeser-Chen, Pavel Pidlypenskyi, Rohit Pandey, Andrea Tagliasacchi, Sameh Khamis, David Kim, Mingsong Dou, Kaiwen Guo, Danhang Tang, Shahram IzadiGenerative Adversarial NetworksJun-Yan Zhu, Taesung Park, Mihaela Rosca, Phillip Isola, Ian Goodfellow

Google at NAACL

Friday, June 8, 2018

Posted by Kenton Lee, Research Scientist and Slav Petrov, Principal Scientist, Language Team, Google AINorth American Association of Computational LinguisticsWidening Natural Language Processing Workshop

Googler Margaret Mitchell setting up our telepresence robots for remote presenters Diana Gonazelez and Gibran Fuentes Pineda to remotely present their first-place work on visual storytelling from Universidad Nacional Autonoma de Mexico.

Test of Time AwardblueBLEU: a Method for Automatic Evaluation of Machine TranslationKishore Papineni, Salim Roukos, Todd Ward, Wei-Jing ZhuDiscriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron AlgorithmsMichael CollinsThumbs up?: Sentiment Classification using Machine Learning TechniquesBo Pang, Lillian Lee, Shivakumar VaithyanathanblueGoogle AI Language Team pageArea Chairs include:Dan Bikel, Dilek Hakkani-Tur, Zornitsa Kozareva, Marius Pasca, Emily Pitler, Idan Szpektor, Taro WatanabePublications Co-ChairMargaret MitchellKeynoteGoogle Assistant or My Assistant? Towards Personalized Situated Conversational AgentsDilek Hakkani-TürPublicationsBootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement LearningPararth Shah, Dilek Hakkani-Tür, Bing Liu, Gokhan TürSHAPED: Shared-Private Encoder-Decoder for Text Style AdaptationYe Zhang, Nan Ding, Radu SoricutOlive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds Using Paraphrases in a Neural ModelVered Schwartz, Chris WatersonAre All Languages Equally Hard to Language-Model?Ryan Cotterell, Sebastian J. Mielke, Jason Eisner, Brian RoarkSelf-Attention with Relative Position RepresentationsPeter Shaw, Jakob Uszkoreit, Ashish VaswaniDialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue SystemsBing Liu, Gokhan Tür, Dilek Hakkani-Tür, Parath Shah, Larry HeckWorkshopsSubword & Character Level Models in NLPManaal Faruqui, Hinrich Schütze, Isabel Trancoso, Yulia Tsvetkov, Yadollah YaghoobzadehStorytelling WorkshopMargaret Mitchell, Ishan Misra, Ting-Hao 'Kenneth' Huang, Frank FerraroEthics in NLPMichael Strube, Dirk Hovy, Margaret Mitchell, Mark AlfanoNAACL HLT PanelsCareers in IndustryPhilip Resnik (moderator), Jason Baldridge, Laura Chiticariu, Marie Mateer, Dan RothEthics in NLPDirk Hovy (moderator), Margaret Mitchell, Vinodkumar Prabhakaran, Mark Yatskar, Barbara Plank

Realtime tSNE Visualizations with TensorFlow.js

Thursday, June 7, 2018

Posted by Nicola Pezzotti, Software Engineering Intern, Google Züricht-distributed Stochastic Neighbor EmbeddingTensorFlow Embedding ProjectorTensorBoardLinear tSNE Optimization for the WebWebGLreleasing this work as an open source libraryTensorFlow.js

Real-time evolution of the tSNE embedding for the complete MNIST dataset with our technique. The dataset contains images of 60,000 handwritten digits. You can find a live demo here.

objective functionN-body simulationBarnes-HutMNIST

Rendering of the three functions used to approximate the repulsive effect created by a single point. In the above figure the repulsive forces show a point in a blue area is pushed to the left/bottom, while a point in the red area is pushed to the right/top while a point in the white region will not move.

This animation shows the evolution of the tSNE embedding (upper left) and of the scalar fields used to approximate its gradient with normalization term (upper right), horizontal shift (bottom left) and vertical shift (bottom right).

TensorFlow.jsan open source libraryFuture WorkN-body simulationsAcknowledgementsWe would like to thank Alexander Mordvintsev, Yannick Assogba, Matt Sharifi, Anna Vilanova, Elmar Eisemann, Nikhil Thorat, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Alessio Bazzica, Boudewijn Lelieveldt, Thomas Höllt, Baldur van Lew, Julian Thijssen and Marvin Ritter.

Announcing an updated YouTube-8M, and the 2nd YouTube-8M Large-Scale Video Understanding Challenge and Workshop

Tuesday, June 5, 2018

Posted by Joonseok Lee, Software Engineer, Google AIYouTube-8M Large-Scale Video Understanding ChallengeKaggleYouTube-8M datasetworkshop at CVPR’17update to the YouTube-8M dataseta new Kaggle video understanding challenge2nd Workshop on YouTube-8M Large-Scale Video Understanding2018 European Conference on Computer Vision

An Updated YouTube-8M Dataset (2018 Edition)ground truthstarter codeTensorFlowThe 2nd YouTube-8M Video Understanding Challenge2nd YouTube-8M Video Understanding ChallengeKaggle competition pageThe 2nd Workshop on YouTube-8M Large-Scale Video UnderstandingECCV’18workshop pageAcknowledgementsThis post reflects the work of many machine perception researchers including Sami Abu-El-Haija, Ke Chen, Nisarg Kothari, Joonseok Lee, Hanhan Li, Paul Natsev, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, as well as Sohier Dane, Julia Elliott, Wendy Kan and Walter Reade from Kaggle. We are also grateful for the support and advice from our partners at YouTube.

Improving Deep Learning Performance with AutoAugment

Monday, June 4, 2018

Posted by Ekin Dogus Cubuk, Google AI Resident and Barret Zoph, Research Scientist, Google Brain Teamdesign neural network architecturesoptimizersAutoAugment: Learning Augmentation Policies from Datareinforcement learningAugmenting Training Datamixup

Left: An original image from the ImageNet dataset. Right: The same image transformed by a commonly used data augmentation transformation, a horizontal flip about the center.

32street view of house numbers

Left: An original image from the SVHN dataset. Right: The same image transformed by AutoAugment. In this case, the optimal transformation was a result of shearing the image and inverting the colors of the pixels.

CIFAR-10ImageNet

Left: An original image from the ImageNet dataset. Right: The same image transformed by the AutoAugment policy. First, the image contrast is maximized, after which the image is rotated.

ResultsStanford CarsFGVC-Aircraftappendix of the paperAcknowledgementsSpecial thanks to the co-authors of the paper Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. We’d also like to thank Alok Aggarwal, Gabriel Bender, Yanping Huang, Pieter-Jan Kindermans, Simon Kornblith, Augustus Odena, Avital Oliver, and Colin Raffel for their help with this project.

Advances in Semantic Textual Similarity

Thursday, May 17, 2018

Posted by Yinfei Yang, Software Engineer and Chris Tar, Engineering Manager, Google AISmart ComposeTalk to BooksTensorFlow HubSemantic Textual SimilarityLearning Semantic Textual Similarity from Conversations

Sentences are semantically similar if they can be answered by the same responses. Otherwise, they are semantically different.

SNLIentailmentSTSBenchmarkCQA task B

For a given input, classification is considered a ranking problem against potential candidates.

Universal Sentence EncoderUniversal Sentence Encoderskip-thought

Pairwise semantic similarity comparison via outputs from TensorFlow Hub Universal Sentence Encoder.

deep average networkTransformer

Multi-task training as described in “Universal Sentence Encoder”. A variety of tasks and task structures are joined by shared encoder layers/parameters (grey boxes).

New Models modelnewTensorFlow HubUniversal Sentence Encoder - LargeUniversal Sentence Encoder - Lite

The Large model is trained with the Transformer encoder described in our second paper. It targets scenarios requiring high precision semantic representations and the best model performance at the cost of speed & size.

The Lite model is trained on a Sentence Piece vocabulary instead of words in order to significantly reduce the vocabulary size, which is a major contributor of model size. It targets scenarios where resources like memory and CPU are limited, such as on-device or browser based implementations.

AcknowledgementsDaniel Cer, Mario Guajardo-Cespedes, Sheng-Yi Kong, Noah Constant for training the models, Nan Hua, Nicole Limtiaco, Rhomni St. John for transferring tasks, Steve Yuan, Yunhsuan Sung, Brian Strope, Ray Kurzweil for discussion of the model architecture. Special thanks to Sheng-Yi Kong and Noah Constant for training the Lite model.

Smart Compose: Using Neural Networks to Help Write Emails

Wednesday, May 16, 2018

Posted by Yonghui Wu, Principal Engineer, Google Brain TeamGoogle I/OSmart ComposeSmart Reply

Latency: Since Smart Compose provides predictions on a per-keystroke basis, it must respond ideally within 100ms for the user not to notice any delays. Balancing model complexity and inference speed was a critical issue.

Scale: Gmail is used by more than 1.4 billion diverse users. In order to provide auto completions that are useful for all Gmail users, the model has to have enough modeling capacity so that it is able to make tailored suggestions in subtly different contexts.

Fairness and Privacy: In developing Smart Compose, we needed to address sources of potential bias in the training process, and had to adhere to the same rigorous user privacy standards as Smart Reply, making sure that our models never expose user’s private information. Furthermore, researchers had no access to emails, which meant they had to develop and train a machine learning system to work on a dataset that they themselves cannot read.

Finding the Right Model ngramneural bag-of-wordsRNN languagesequence-to-sequenceword embeddings

Smart Compose RNN-LM model architecture. Subject and previous email message are encoded by averaging the word embeddings in each field. The averaged embeddings are then fed to the RNN-LM at each decoding step.

Accelerated Model Training & ServingTPUv2 PodFairness and PrivacyFairness in machine learningSemantics derived automatically from language corpora contain human-like biasesthis paperFuture workTransformer,RNMT+AcknowledgementsSmart Compose language generation model was developed by Benjamin Lee, Mia Chen, Gagan Bansal, Justin Lu, Jackie Tsay, Kaushik Roy, Tobias Bosch, Yinan Wang, Matthew Dierker, Katherine Evans, Thomas Jablin, Dehao Chen, Vinu Rajashekhar, Akshay Agrawal, Yuan Cao, Shuyuan Zhang, Xiaobing Liu, Noam Shazeer, Andrew Dai, Zhifeng Chen, Rami Al-Rfou, DK Choe, Yunhsuan Sung, Brian Strope, Timothy Sohn, Yonghui Wu, and many others.

Automatic Photography with Google Clips

Friday, May 11, 2018

Posted by Aseem Agarwala, Research Scientist, Clips Content Team LeadTo me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event as well as of a precise organization of forms which give that event its proper expression.Henri Cartier-BressonGoogle Clips

We wanted all computations to be performed on-device. In addition to extending battery life and reducing latency, on-device processing means that none of your clips leave the device unless you decide to save or share them, which is a key privacy control.

We wanted the device to capture short videos, rather than single photographs. Moments with motion can be more poignant and true-to-memory, and it is often easier to shoot a video around a compelling moment than it is to capture a perfect, single instant in time.

We wanted to focus on capturing candid moments of people and pets, rather than the more abstract and subjective problem of capturing artistic images. That is, we did not attempt to teach Clips to think about composition, color balance, light, etc.; instead, Clips focuses on selecting ranges of time containing people and animals doing interesting activities.

Learning to Recognize Great MomentsWe then hired

Training a Clips Quality Modelinrecognize over 27,000 different labelsMobileNetpiecewise linear regression model

Diagram of the training process for generating frame quality scores. Piecewise linear regression maps from an ICM embedding to a score which, when averaged across a video segment, yields a moment score. The moment score of the preferred segment should be higher.

most recent releaseShot Control

Respect Power & Thermals: We want the Clips battery to last roughly three hours, and we don’t want the device to overheat — the device can’t run at full throttle all the time. Clips spends much of its time in a low-power mode that captures one frame per second. If the quality of that frame exceeds a threshold set by how much Clips has recently shot, it moves into a high-power mode, capturing at 15 fps. Clips then saves a clip at the first quality peak encountered.

Avoid Redundancy: We don’t want Clips to capture all of its moments at once, and ignore the rest of a session. Our algorithms therefore cluster moments into visually similar groups, and limit the number of clips in each cluster.

The Benefit of Hindsight: It’s much easier to determine which clips are the best when you can examine the totality of clips captured. Clips therefore captures more moments than it intends to show to the user. When clips are ready to be transferred to the phone, the Clips device takes a second look at what it has shot, and only transfers the best and least redundant content.

Machine Learning Fairnessfairness in ML algorithmsConclusionAcknowledgementsThe algorithms described here were conceived and implemented by a large group of Google engineers, research scientists, and others. Figures were made by Lior Shapira. Thanks to Lior and Juston Payne for video content.

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