DINO-QPM adapts frozen DINOv2 models via average-pooled patch embeddings and a sparsity loss to deliver both higher classification accuracy and human-interpretable global explanations.
Towards Faithfully Inter- pretable NLP Systems: How should we define and evaluate faithfulness?, 2020
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DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification
DINO-QPM adapts frozen DINOv2 models via average-pooled patch embeddings and a sparsity loss to deliver both higher classification accuracy and human-interpretable global explanations.