Timezone: »
Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied, prompting us to ask the question: what is the best way to sample episodes? In this paper, we first propose a method to approximate episode sampling distributions based on their difficulty. Building on this method, we perform an extensive analysis and find that sampling uniformly over episode difficulty outperforms other sampling schemes, including curriculum and easy-/hard-mining. As the proposed sampling method is algorithm agnostic, we can leverage these insights to improve few-shot learning accuracies across many episodic training algorithms. We demonstrate the efficacy of our method across popular few-shot learning datasets, algorithms, network architectures, and protocols.
Author Information
Sébastien Arnold (University of Southern California)
Guneet Dhillon (University of Oxford)
Avinash Ravichandran (AWS)
Stefano Soatto (UCLA)
Stefano Soatto received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 1996; he joined UCLA in 2000 after being Assistant and then Associate Professor of Electrical Engineering and Biomedical Engineering at Washington University, and Research Associate in Applied Sciences at Harvard University. Between 1995 and 1998 he was also Ricercatore in the Department of Mathematics and Computer Science at the University of Udine - Italy. He received his D.Ing. degree (highest honors) from the University of Padova- Italy in 1992. His general research interests are in Computer Vision and Nonlinear Estimation and Control Theory. In particular, he is interested in ways for computers to use sensory information to interact with humans and the environment. Dr. Soatto is the recipient of the David Marr Prize for work on Euclidean reconstruction and reprojection up to subgroups. He also received the Siemens Prize with the Outstanding Paper Award from the IEEE Computer Society for his work on optimal structure from motion. He received the National Science Foundation Career Award and the Okawa Foundation Grant. He is a Member of the Editorial Board of the International Journal of Computer Vision (IJCV) and Foundations and Trends in Computer Graphics and Vision. He is the founder and director of the UCLA Vision Lab; more information is available at http://vision.ucla.edu
Related Events (a corresponding poster, oral, or spotlight)
-
2021 Spotlight: Uniform Sampling over Episode Difficulty »
Dates n/a. Room
More from the Same Authors
-
2021 Spotlight: Long Short-Term Transformer for Online Action Detection »
Mingze Xu · Yuanjun Xiong · Hao Chen · Xinyu Li · Wei Xia · Zhuowen Tu · Stefano Soatto -
2022 Poster: On Leave-One-Out Conditional Mutual Information For Generalization »
Mohamad Rida Rammal · Alessandro Achille · Aditya Golatkar · Suhas Diggavi · Stefano Soatto -
2022 : Evaluating Worst Case Adversarial Weather Perturbations Robustness »
Yihan Wang · Yunhao Ba · Howard Zhang · Huan Zhang · Achuta Kadambi · Stefano Soatto · Alex Wong · Cho-Jui Hsieh -
2023 Poster: Gacs-Korner Common Information Variational Autoencoder »
Michael Kleinman · Alessandro Achille · Stefano Soatto · Jonathan Kao -
2023 Poster: Your representations are in the network: composable and parallel adaptation for large scale models »
Yonatan Dukler · Alessandro Achille · Hao Yang · Varsha Vivek · Luca Zancato · Benjamin Bowman · Avinash Ravichandran · Charless Fowlkes · Ashwin Swaminathan · Stefano Soatto -
2022 Poster: Semi-supervised Vision Transformers at Scale »
Zhaowei Cai · Avinash Ravichandran · Paolo Favaro · Manchen Wang · Davide Modolo · Rahul Bhotika · Zhuowen Tu · Stefano Soatto -
2021 Poster: Long Short-Term Transformer for Online Action Detection »
Mingze Xu · Yuanjun Xiong · Hao Chen · Xinyu Li · Wei Xia · Zhuowen Tu · Stefano Soatto -
2020 Poster: Predicting Training Time Without Training »
Luca Zancato · Alessandro Achille · Avinash Ravichandran · Rahul Bhotika · Stefano Soatto -
2019 : Coffee/Poster session 2 »
Xingyou Song · Puneet Mangla · David Salinas · Zhenxun Zhuang · Leo Feng · Shell Xu Hu · Raul Puri · Wesley Maddox · Aniruddh Raghu · Prudencio Tossou · Mingzhang Yin · Ishita Dasgupta · Kangwook Lee · Ferran Alet · Zhen Xu · Jörg Franke · James Harrison · Jonathan Warrell · Guneet Dhillon · Arber Zela · Xin Qiu · Julien Niklas Siems · Russell Mendonca · Louis Schlessinger · Jeffrey Li · Georgiana Manolache · Debojyoti Dutta · Lucas Glass · Abhishek Singh · Gregor Koehler -
2019 Poster: Reducing the variance in online optimization by transporting past gradients »
Sébastien Arnold · Pierre-Antoine Manzagol · Reza Babanezhad Harikandeh · Ioannis Mitliagkas · Nicolas Le Roux -
2019 Spotlight: Reducing the variance in online optimization by transporting past gradients »
Sébastien Arnold · Pierre-Antoine Manzagol · Reza Babanezhad Harikandeh · Ioannis Mitliagkas · Nicolas Le Roux -
2018 : Poster Session »
Sujay Sanghavi · Vatsal Shah · Yanyao Shen · Tianchen Zhao · Yuandong Tian · Tomer Galanti · Mufan Li · Gilad Cohen · Daniel Rothchild · Aristide Baratin · Devansh Arpit · Vagelis Papalexakis · Michael Perlmutter · Ashok Vardhan Makkuva · Pim de Haan · Yingyan Lin · Wanmo Kang · Cheolhyoung Lee · Hao Shen · Sho Yaida · Dan Roberts · Nadav Cohen · Philippe Casgrain · Dejiao Zhang · Tengyu Ma · Avinash Ravichandran · Julian Emilio Salazar · Bo Li · Davis Liang · Christopher Wong · Glen Bigan Mbeng · Animesh Garg -
2018 : Plenary Talk 3 »
Stefano Soatto -
2010 Tutorial: Vision-Based Control, Control-Based Vision, and the Information Knot That Ties Them »
Stefano Soatto -
2010 Poster: Occlusion Detection and Motion Estimation with Convex Optimization »
Alper Ayvaci · Michalis Raptis · Stefano Soatto -
2006 Poster: Detecting Humans via Their Pose »
Alessandro Bissacco · Ming-Hsuan Yang · Stefano Soatto