Summary of Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations For Vision Foundation Models, by Hengyi Wang et al.
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
by Hengyi Wang, Shiwei Tan, Hao Wang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 focuses on developing trustworthy explanation methods for Vision Transformers (ViTs), which have gained popularity due to their ability to be jointly trained with large language models and serve as robust vision foundation models. Existing approaches, such as feature-attribution and conceptual models, are inadequate in providing faithful explanations of ViT predictions. The authors propose five desiderata for explaining ViTs: faithfulness, stability, sparsity, multi-level structure, and parsimony, and introduce a variational Bayesian explanation framework called ProbAbilistic Concept Explainers (PACE). PACE models the distributions of patch embeddings to provide post-hoc conceptual explanations that meet the proposed criteria. The paper demonstrates the effectiveness of ViTs by modeling the joint distribution of patch embeddings and predictions. It also shows how PACE’s patch-level explanations bridge the gap between image-level and dataset-level explanations, completing its multi-level structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making it easier to understand why Vision Transformers make certain predictions. These transformers are very good at recognizing images, but we don’t know exactly how they do it. The authors want to fix this by creating a new way to explain their decisions. They propose five important criteria for these explanations and create a special method called PACE. PACE helps us understand what the transformer is focusing on when it makes predictions. It’s like getting a report card for the transformer’s thinking process. The researchers tested PACE on different datasets and showed that it works better than existing methods. |
Keywords
* Artificial intelligence * Transformer * Vit
