BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)

Self-supervised representation learning relies on negative samples to keep the encoder from collapsing to trivial solutions. However, this paper shows that negative samples, which are a nuisance to implement, are not necessary for learning good representation, and their algorithm BYOL is able to outperform other baselines using just positive samples. OUTLINE: 0:00 - Intro & Overview 1:10 - Image Representation Learning 3:55 - Self-Supervised Learning 5:35 - Negative Samples 10:50 - BYOL 23:20 - Experiments 30:10 - Conclusion & Broader Impact Paper: https://ift.tt/30XKSds Abstract: We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods intrinsically rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches 74.3% top-1 classification accuracy on ImageNet using the standard linear evaluation protocol with a ResNet-50 architecture and 79.6% with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Authors: Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, Michal Valko Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB