Summary of Intriguing Equivalence Structures Of the Embedding Space Of Vision Transformers, by Shaeke Salman and Md Montasir Bin Shams and Xiuwen Liu
Intriguing Equivalence Structures of the Embedding Space of Vision Transformers
by Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the representation space of pre-trained large foundation models, which have achieved remarkable performance on benchmark datasets. However, these models’ complexity hinders our understanding of their internal workings. The authors investigate the vision transformers as a case study and find that the representation space consists of piecewise linear subspaces with inputs sharing similar representations, as well as local normal spaces where visually indistinguishable inputs exhibit distinct representations. These findings have implications for downstream models, which can lead to overgeneralization and limited semantic generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how big AI models work. It looks at pre-trained models that do well on tests and real-world applications. But these models are hard to understand because they’re really complex. The researchers focused on a type of model called vision transformers, which process images. They found that the way these models represent images is made up of many small pieces that can be very different or very similar. This has important implications for how these models work and what we can expect from them. |
Keywords
* Artificial intelligence * Generalization
