Summary of Revisiting Disentanglement in Downstream Tasks: a Study on Its Necessity For Abstract Visual Reasoning, by Ruiqian Nai et al.
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
by Ruiqian Nai, Zixin Wen, Ji Li, Yuanzhi Li, Yang Gao
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 investigates the necessity of disentangled representations in downstream applications, specifically abstract visual reasoning. Researchers have advocated for leveraging disentangled representations to complete tasks with encouraging empirical evidence. However, this paper shows that dimension-wise disentangled representations are unnecessary on a fundamental downstream task, abstract visual reasoning. The study provides extensive empirical evidence against the necessity of disentanglement, covering multiple datasets, representation learning methods, and downstream network architectures. In addition, the findings suggest that the informativeness of representations is a better indicator of downstream performance than disentanglement. Furthermore, the positive correlation between informativeness and disentanglement explains the claimed usefulness of disentangled representations in previous works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether we really need to use special representations called “disentangled” to do certain tasks with images. Some people think these representations are important for doing things like recognizing objects, but this study shows that they’re not actually necessary. The researchers tested many different ways of making these representations and using them in computer vision tasks. They found that the information contained in the representation is what’s really important, not whether it’s “disentangled” or not. This is an important finding because it helps us understand how to make better models for recognizing objects. |
Keywords
* Artificial intelligence * Representation learning
