An Novel Interpretable Fine-grained Image Classification Model Based on Improved Neural Prototype Tree
Jin'an Cui, Jinghao Gong, Guangchen Wang, Jinbao Li, Xiaoyu Liu, Song Liu · 2023
The fine-grained image classification task is a major task in computer vision. Although many deep learning inter-pretable models have been proposed for this task, the accuracy and interpretability of these models need to be improved. We propose an interpretable fine-grained image classification model based on an improved neural prototype tree. In our model, we design the new multi-grained feature extraction network with three new backbone networks to extract features of fine-grained and multi-grained images more effectively. Furthermore, we design a new background prototype removing mechanism in the soft neural binary decision tree layer to optimize prototype path decision. Afterwards, we design a new loss function with both a leaf node loss function and a fully connected layer loss function to improve the generalization ability. Finally, we evaluate our model on three public datasets CUB-200-2011, FGVC-Aircraft, and Chest X-ray to compare with other baseline models.