Contrastive learning in unsupervised learning with Prannay Khosla

Weights & Biases · Advanced ·📰 AI News & Updates ·5y ago
Contrastive learning in unsupervised learning has seen a resurgence in recent years, leading to State of the art performance in training deep image models. We discuss the ideas behind such a setup, the theoretical grounding of the ideas. Further we discuss extensions of contrastive learning to a fully supervised setting that allows us to leverage label information for better representation learning. We analyze various forms of the loss functions, the form of the gradients as well as results which demonstrate state of the art top-1 performance on ImageNet, robustness to corruptions, stability with respect to hyperparameters and better performance in reduced data settings. Prannay Khosla is an AI Resident in Google AI, Perception. He works primarily on representation learning and robustness. Previously he has worked at Microsoft Research, Max Planck Institute of Technology for Intelligent Systems, IIT Kanpur, Nutanix and holds an undergrad degree from IIT Kanpur. This presentation was a part of our Virtual Deep Learning Salon, to sign up for updates on these join our meetup and slack community! Meetup: http://wandb.me/meetups Slack: http://wandb.me/fs 👩🏼‍🚀Weights and Biases: Record and visualize every detail of your research, collaborate easily, advance the state of the art - we’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://wandb.ai/site/articles - Gallery: See what you can create with W&B - https://wandb.ai/fully-connected - Continue the conversation on our slack community - http://wandb.me/fs
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