@inproceedings{
ma2024interpretable,
title={Interpretable Image Classification with Adaptive Prototype-based Vision Transformers},
author={Chiyu Ma and Jon Donnelly and Wenjun Liu and Soroush Vosoughi and Cynthia Rudin and Chaofan Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hjhpCJfbFG}
}
We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that.'' In our model, a prototype consists of **parts**, which can deform over irregular geometries to create a better comparison between images. Unlike existing models that rely on Convolutional Neural Network (CNN) backbones and spatially rigid prototypes, our model integrates Vision Transformer (ViT) backbones into prototype based models, while offering spatially deformed prototypes that not only accommodate geometric variations of objects but also provide coherent and clear prototypical feature representations with an adaptive number of prototypical parts. Our experiments show that our model can generally achieve higher performance than the existing prototype based models. Our comprehensive analyses ensure that the prototypes are consistent and the interpretations are faithful.
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
Chiyu Ma Jon Donnelly Wenjun Liu Soroush Vosoughi Cynthia Rudin Chaofan Chen