Exploring Simple Siamese Representation Learning
Автор: Connor Shorten
Загружено: 2020-11-25
Просмотров: 10172
Описание:
What makes contrastive learning work so well? This paper highlights the contribution of the Siamese architecture as a compliment to data augmentation and shows how Siamese nets + a stop-gradient operation in the negative encoder is all you need for strong contrastive self-supervised learning results. The paper also presents an interesting k-Means style explanation of the optimization problem contrastive self-supervised learning solves. Thanks for watching! Please Subscribe!
Paper Links:
SimSiam: https://arxiv.org/pdf/2011.10566.pdf
SimCLR: https://arxiv.org/pdf/2002.05709.pdf
MoCo: https://arxiv.org/pdf/1911.05722.pdf
SwAV: https://arxiv.org/pdf/2006.09882.pdf
BYOL: https://arxiv.org/pdf/2006.07733.pdf
kMeans: https://stanford.edu/~cpiech/cs221/ha...
(Not mentioned in video, but here's an interesting tutorial on coding siamese nets: https://www.pyimagesearch.com/2020/11...)
Thanks for watching!
Chapters
0:00 Introduction
1:16 SimSiam Architecture
2:42 Representation Collapse
3:41 SimCLR
4:50 MoCo
5:54 BYOL
7:00 SwaV (Clustering)
8:20 Unifying View
9:33 k-Means optimization problem
11:06 Results
12:20 Key Takeaways
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