DINO: Emerging Properties in Self-Supervised Vision Transformers (Facebook AI Research Explained) скачать в хорошем качестве

DINO: Emerging Properties in Self-Supervised Vision Transformers (Facebook AI Research Explained)

#dino #facebook #selfsupervised Self-Supervised Learning is the final frontier in Representation Learning: Getting useful features without any labels. Facebook AI's new system, DINO, combines advances in Self-Supervised Learning for Computer Vision with the new Vision Transformer (ViT) architecture and achieves impressive results without any labels. Attention maps can be directly interpreted as segmentation maps, and the obtained representations can be used for image retrieval and zero-shot k-nearest neighbor classifiers (KNNs). OUTLINE: 0:00 - Intro & Overview 6:20 - Vision Transformers 9:20 - Self-Supervised Learning for Images 13:30 - Self-Distillation 15:20 - Building the teacher from the student by moving average 16:45 - DINO Pseudocode 23:10 - Why Cross-Entropy Loss? 28:20 - Experimental Results 33:40 - My Hypothesis why this works 38:45 - Conclusion & Comments Paper: https://arxiv.org/abs/2104.14294 Blog:   / dino-paws-computer-vision-with-self-superv...   Code: https://github.com/facebookresearch/dino My Video on ViT:    • An Image is Worth 16x16 Words: Transf...   My Video on BYOL:    • BYOL: Bootstrap Your Own Latent: A Ne...   Abstract: In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-supervised ViT features contain explicit information about the semantic segmentation of an image, which does not emerge as clearly with supervised ViTs, nor with convnets. Second, these features are also excellent k-NN classifiers, reaching 78.3% top-1 on ImageNet with a small ViT. Our study also underlines the importance of momentum encoder, multi-crop training, and the use of small patches with ViTs. We implement our findings into a simple self-supervised method, called DINO, which we interpret as a form of self-distillation with no labels. We show the synergy between DINO and ViTs by achieving 80.1% top-1 on ImageNet in linear evaluation with ViT-Base. Authors: Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube:    / yannickilcher   Twitter:   / ykilcher   Discord:   / discord   BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn:   / yannic-kilcher-488534136   BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon:   / yannickilcher   Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n