Regularizing a deep network's local explanations to match human rationales during training improves accuracy on out-of-distribution text data without hurting in-distribution test accuracy.
Towards explanation of dnn-based prediction with guided feature inversion,
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Learning Credible Deep Neural Networks with Rationale Regularization
Regularizing a deep network's local explanations to match human rationales during training improves accuracy on out-of-distribution text data without hurting in-distribution test accuracy.