HQProtoPNet: An Evidence-Based Model for Interpretable Image Recognition

In image recognition, improving the interpretability of the recognition model can help people understand the model better and increase the trust of human beings for model prediction. The prototype-based interpretable model is a self-explanatory image recognition model that simulates the evidence reasoning used in human recognition. Each prototype is evidence that contains category features, which can help in determining the image category. Based on the prototype-based model, this paper introduces a deep interpretable network architecture called the high-quality prototypical part network (HQProtoPNet). Compared to existing work, this paper adds random erasing to enhance the picture, helping to improve prototype generation and increase model prediction. The multiple scale conversion operation is also introduced and the similarity calculation is improved to make the prototype have multiscale information and matching ability. Furthermore, the accuracy of HQProtoPNet can reach or even exceed the accuracy of several black-box models. Additionally, due to the improvement in the quality of the prototype, the model's prediction accuracy is improved by stacking without reducing the interpretability of the stacked model, which gives the model real stackability.