SIDE produces sparse prototype-based explanations for image classifiers, cutting explanation size by over 90 percent while keeping accuracy comparable to InfoDisent.
Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet
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SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
SIDE produces sparse prototype-based explanations for image classifiers, cutting explanation size by over 90 percent while keeping accuracy comparable to InfoDisent.