Summary of On Partial Prototype Collapse in the Dino Family Of Self-supervised Methods, by Hariprasath Govindarajan et al.
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
by Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a solution to the “representation collapse” problem in self-supervised learning by regularizing the distribution of data points over clusters. The authors show that existing methods, such as DINO, can still suffer from “prototype redundancy” even when representation collapse is avoided. This issue leads to shortcuts and less informative representations. To mitigate this, the paper suggests encouraging diverse prototypes, enabling more fine-grained clustering and more informative representations. Experimental results demonstrate the effectiveness of this approach on long-tailed fine-grained datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how self-supervised learning methods can get stuck in a problem called “representation collapse”. They find that even when they avoid this issue, another problem occurs: some prototypes become redundant and make it easier for the method to produce less accurate results. To fix this, the authors suggest making each prototype unique and useful, which helps clustering and makes representations more informative. This works especially well when training on datasets with many different categories. |
Keywords
» Artificial intelligence » Clustering » Self supervised
