MDM:Visual Explanations for Neural Networks via Multiple Dynamic Mask

The Class Activation Maps(CAM) lookup of a neural network can tell us what regions the neural network is focusing on when making a decision.We propose an algorithm Multiple Dynamic Mask (MDM), which is a general saliency graph query method with interpretability of inference process. The algorithm is based on an assumption: when a picture is input into a trained neural network, only 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, and the reasoning process conforms to human cognition. 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. The algorithm is based on learning 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 ResNet and DenseNet as the trained neural network. The recent advanced saliency map search method and the results of MDM on the performance indicators of each search effect item, the performance of MDM has reached the state of the art. We applied the MDM method to the interpretable neural network ProtoPNet and XProtoNet, which improved the model's interpretability prototype search performance. And we visualize the effect of convolutional neural architecture and Transformer architecture in saliency map search, illustrating the interpretability and generality of MDM.