Attention-Based Prototypical Learning Towards Interpretable, Confident and Robust Deep Neural Networks
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We propose a new framework for prototypical learning that bases decision-making on few relevant examples that we call prototypes. Our framework utilizes an attention mechanism that relates the encoded representations to determine the prototypes. This results in a model that: (1) enables interpretability by outputting samples most relevant to the decision-making in addition to outputting the classification results; (2) allows confidence-controlled prediction by quantifying the mismatch across prototype labels; (3) permits detection of distribution mismatch; and (4) improves robustness to label noise. We demonstrate that our model is able to maintain comparable performance to baseline models while enabling all these benefits.
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