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.
Synthesizing the preferred inputs for neurons in neural networks via deep generator net- works
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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.