Summary of Beyond Scalars: Concept-based Alignment Analysis in Vision Transformers, by Johanna Vielhaben et al.
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
by Johanna Vielhaben, Dilyara Bareeva, Jim Berend, Wojciech Samek, Nils Strodthoff
First submitted to arxiv on: 9 Dec 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 proposes a new approach to comparing and understanding the features learned by vision transformers (ViTs) trained using various learning paradigms, including fully supervised and self-supervised methods. The current alignment measures used to compare these feature spaces can be misleading as they provide only a single scalar value, hiding the differences between common and unique features. To address this limitation, the authors combine alignment analysis with concept discovery, allowing for a fine-grained comparison of the concepts encoded in each feature space. This novel approach reveals both universal and unique concepts across different representations, as well as their internal structure. The paper defines concepts as arbitrary manifolds that capture the geometry of the feature space and uses a generalized Rand index to measure distances between concept proximity scores. A sanity check confirms the superiority of this new approach over existing linear baselines. The authors apply this method to four ViTs with varying levels of supervision, finding that increased supervision correlates with a reduction in the semantic structure of learned representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how different computer vision models learn features from images. These models are trained using different approaches, and it’s hard to compare them directly because they learn unique features. The authors develop a new way to analyze these features by breaking them down into smaller concepts that capture the relationships between them. This approach shows us both common and special ideas learned by each model, as well as how they’re structured inside. By applying this method to four different models, the authors find that more supervised learning leads to less complex representations. |
Keywords
» Artificial intelligence » Alignment » Self supervised » Supervised
