Summary of Bi-ice: An Inner Interpretable Framework For Image Classification Via Bi-directional Interactions Between Concept and Input Embeddings, by Jinyung Hong et al.
Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddings
by Jinyung Hong, Yearim Kim, Keun Hee Park, Sangyu Han, Nojun Kwak, Theodore P. Pavlic
First submitted to arxiv on: 26 Nov 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 presents a conceptual framework for inner interpretability and multilevel analysis in large-scale image classification tasks. It introduces the Bi-directional Interaction between Concept and Input Embeddings (Bi-ICE) module, which facilitates interpretability across computational, algorithmic, and implementation levels. The module enhances transparency by generating predictions based on human-understandable concepts, quantifying their contributions, and localizing them within inputs. This is achieved through a process of concept learning and its convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how AI systems work at a deeper level. It’s like trying to figure out how your brain works by looking inside it. The researchers created a special tool called Bi-ICE that lets us see how AI models make decisions and what they’re thinking about when they look at pictures. This can help us build better AI systems that are more transparent and easier to understand. |
Keywords
» Artificial intelligence » Image classification
