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In this video I cover DINO (self DIstillation with NO labels) introduced in the "Emerging Properties in Self-Supervised Vision Transformers" paper by Facebook AI.
The idea is to see whether using supervised learning was preventing transformers from showing the same kind of results in CV as they demonstrated in the NLP world (where we use self-supervised learning objectives such as (masked) language modeling).
It turns out some nice properties emerge such as:
* DINO-ViT learns to predict segmentation masks
* features are especially of high quality for the k-NN classification
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✅ Paper: https://arxiv.org/abs/2104.14294
✅ Code: https://github.com/facebookresearch/dino
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⌚️ Timetable:
00:00 DINO main ideas, attention maps explained
05:05 DINO explained in depth
10:30 Pseudocode walk-through
13:55 Multi-crop and local-to-global correspondence
15:15 More details on the teacher network
19:00 Results
25:00 Ablations
27:40 Collapse analysis
30:40 Features visualized and outro
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#dino #facebook #selfsupervised