DINO: Self-Supervised Vision Transformers

DINO, a remarkable self-supervised method, employs two distinct augmented views of an image to acquire the ability to concentrate on objects within the image and generate distinguishable representations for various image categories. It has outperformed prior self-supervised techniques across a range of vision tasks and impressively achieved an 80.1% accuracy on ImageNet, all while utilizing the ViT-B as its backbone.
Paper link: arxiv.org/abs/2104.14294
Table of Content:
00:00 Introduction
03:45 Knowledge Distillation
05:13 DINO
07:40 Multi-crop training
12:31 Avoiding Collapse
16:06 Results
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[@yiqian22]

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[@soroushmehraban]

My pleasure!

[@ericsy78]

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[@soroushmehraban]

Thanks for the feedback! It’s my pleasure

[@alihadimoghadam8931]

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[@soroushmehraban]

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[@AshishJain-iw5md]

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[@soroushmehraban]

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[@soroushmehraban]

Thanks for the kind comment! This is really encouraging. Will try my best to come up with more paper reviews in the future.