Abstract
We propose a new framework for prototypical learning that bases decision-making on few relevant examples that we call prototypes. Our framework, which can be integrated into a wide range of neural network architectures including pre-trained models, utilizes an attention mechanism that relates the encoded representations to determine the prototypes. This results in a model that: (1)enables high-quality interpretability by outputting samples most relevant to the decision-making in addition to outputting the classification results; (2)allows state-of-the-art confidence estimation by quantifying the mismatch across prototype labels; (3)permits state-of-the-art detection of distribution mismatch; and (4)improves robustness to label noise. We demonstrate that our model can enable all these benefits without sacrificing accuracy.
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URL
http://arxiv.org/abs/1902.06292