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Humans are able to explain their reasoning. On the contrary, deep neuralnetworks are not. This paper attempts to bridge this gap by introducing a newway to design interpretable neural networks for classification, inspired byphysiological evidence of the human visual system's inner-workings. This paperproposes a neural network design paradigm, termed InterpNET, which can becombined with any existing classification architecture to generate naturallanguage explanations of the classifications. The success of the module relieson the assumption that the network's computation and reasoning is representedin its internal layer activations. While in principle InterpNET could beapplied to any existing classification architecture, it is evaluated via animage classification and explanation task. Experiments on a CUB birdclassification and explanation dataset show qualitatively and quantitativelythat the model is able to generate high-quality explanations. While the currentstate-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves amuch higher METEOR score of 37.9.

InterpNET: Neural Introspection for Interpretable Deep Learning