DProtoNet: Decoupling the inference module and the explanation module enables neural networks to have better accuracy and interpretability

• URL: http://arxiv.org/abs/2210.08336v1
• Date: Sat, 15 Oct 2022 17:05:55 GMT
• Title: DProtoNet: Decoupling the inference module and the explanation module enables neural networks to have better accuracy and interpretability
• Authors: Yitao Peng, Yihang Liu, Longzhen Yang, Lianghua He
• Abstract summary: In the previous method, by modifying the architecture of the neural network, the network simulates the human reasoning process. We propose DProtoNet (Decoupling Prototypical network), it stores the decision basis of the neural network by using feature masks. It decouples the neural network inference module from the interpretation module, and removes the specific architectural limitations of the interpretable network.
• Score: 5.333582981327497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interpretation of decisions made by neural networks is the focus of recent research. In the previous method, by modifying the architecture of the neural network, the network simulates the human reasoning process, that is, by finding the decision elements to make decisions, so that the network has the interpretability of the reasoning process. The specific interpretable architecture will limit the fitting space of the network, resulting in a decrease in the classification performance of the network, unstable convergence, and general interpretability. We propose DProtoNet (Decoupling Prototypical network), it stores the decision basis of the neural network by using feature masks, and it uses Multiple Dynamic Masks (MDM) to explain the decision basis for feature mask retention. It decouples the neural network inference module from the interpretation module, and removes the specific architectural limitations of the interpretable network, so that the decision-making architecture of the network retains the original network architecture as much as possible, making the neural network more expressive, and greatly improving the interpretability. Classification performance and interpretability of explanatory networks. We propose to replace the prototype learning of a single image with the prototype learning of multiple images, which makes the prototype robust, improves the convergence speed of network training, and makes the accuracy of the network more stable during the learning process. We test on multiple datasets, DProtoNet can improve the accuracy of recent advanced interpretable network models by 5% to 10%, and its classification performance is comparable to that of backbone networks without interpretability. It also achieves the state of the art in interpretability performance.

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