TEXTER generates zero-shot textual explanations for image classifiers by isolating decision-critical features from contributing neurons, mapping them into CLIP space, and using sparse autoencoders for improved interpretability in Transformers.
Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
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Zero-Shot Textual Explanations via Translating Decision-Critical Features
TEXTER generates zero-shot textual explanations for image classifiers by isolating decision-critical features from contributing neurons, mapping them into CLIP space, and using sparse autoencoders for improved interpretability in Transformers.