Summary of Faithful and Plausible Natural Language Explanations For Image Classification: a Pipeline Approach, by Adam Wojciechowski et al.
Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach
by Adam Wojciechowski, Mateusz Lango, Ondrej Dusek
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposed post-hoc natural language explanation method for CNN-based classifiers generates faithful descriptions of their decision processes without affecting predictive performance. The approach analyzes influential neurons and activation maps to produce a structured meaning representation, which is then converted into text by a language model. This pipeline method provides accurate insights into the classification process while remaining accessible to non-experts. Experimental results show that the generated explanations are significantly more plausible and faithful than baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps explain how computers make decisions when shown images. Right now, it’s hard to understand why these decisions were made. The researchers came up with a new way to explain these decisions using words. They looked at which parts of the computer’s brain are most important for making decisions and used this information to create explanations that are easy to understand. |
Keywords
* Artificial intelligence * Classification * Cnn * Language model
