Explaining the result for an image classification
Explaining the result for an image classification
Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
arXiv paper abstract https://arxiv.org/abs/2106.01440v1
arXiv PDF paper https://arxiv.org/pdf/2106.01440v1.pdf
… Due to their black-box .., deep learning techniques are not yet widely adopted … for … critical domains, like healthcare and justice.
… presents Memory Wrap, a plug-and-play extension to any image classification model.
… adopting a content-attention mechanism
… outperforms standard classifiers when it learns from a limited set of data … comparable performance when it learns from the full dataset.
… get insights about its decision process.
… test … on … CIFAR10, SVHN, and CINIC10.
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