Abstract
The active region lookup of a neural network tells us which regions the neural network focuses on when making a decision, which gives us a basis for interpretability when the neural network makes a classification decision. We propose an algorithm Multiple Dynamic Mask(MDM), which is a general saliency graph query method with interpretability of the inference process. Its proposal is based on an assumption: when a picture is input to a neural network that has been trained, the activation features related to classification will affect the classification results of the neural network, and the features unrelated to classification will hardly affect the classification results of the network. MDM: A learning-based end-to-end algorithm for finding regions of interest for neural network classification. It has the following advantages: 1. It has the interpretability of the reasoning process. 2. It is universal, it can be used for any neural network and does not depend on the internal structure of the neural network. 3. The search performance is better. Because the algorithm is based on learning to generate masks and has the ability to adapt to different data and networks, the performance is better than the method proposed in the previous paper. For the MDM saliency map search algorithm, we experimentally compared the performance indicators of various saliency map search methods and the MDM with ResNet and DenseNet as the trained neural networks. The search effect performance of the MDM reached the state of the art. We applied the MDM to the interpretable neural network ProtoPNet and XProtoNet, which improved the interpretability of the model and the prototype search performance. We visualize the performance of convolutional neural architecture and Transformer architecture on saliency map search.
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URL
https://arxiv.org/abs/2207.08046
