Post-hoc Part-Prototype Networks

Andong Tan, Fengtao Zhou, Hao Chen
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:47681-47693, 2024.

Abstract

Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal ”where” a model looks at for a given input, fail to explain ”what” the model looks for (e.g., what is important to classify a bird image to a Scott Oriole?). Existing part-prototype networks leverage part-prototypes (e.g., characteristic Scott Oriole’s wing and head) to answer both ”where" and ”what", but often under-perform their black box counterparts in the accuracy. Therefore, a natural question is: can one construct a network that answers both ”where” and ”what" in a post-hoc manner to guarantee the model’s performance? To this end, we propose the first post-hoc part-prototype network via decomposing the classification head of a trained model into a set of interpretable part-prototypes. Concretely, we propose an unsupervised prototype discovery and refining strategy to obtain prototypes that can precisely reconstruct the classification head, yet being interpretable. Besides guaranteeing the performance, we show that our network offers more faithful explanations qualitatively and yields even better part-prototypes quantitatively than prior part-prototype networks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-tan24g, title = {Post-hoc Part-Prototype Networks}, author = {Tan, Andong and Zhou, Fengtao and Chen, Hao}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {47681--47693}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/tan24g/tan24g.pdf}, url = {https://proceedings.mlr.press/v235/tan24g.html}, abstract = {Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal ”where” a model looks at for a given input, fail to explain ”what” the model looks for (e.g., what is important to classify a bird image to a Scott Oriole?). Existing part-prototype networks leverage part-prototypes (e.g., characteristic Scott Oriole’s wing and head) to answer both ”where" and ”what", but often under-perform their black box counterparts in the accuracy. Therefore, a natural question is: can one construct a network that answers both ”where” and ”what" in a post-hoc manner to guarantee the model’s performance? To this end, we propose the first post-hoc part-prototype network via decomposing the classification head of a trained model into a set of interpretable part-prototypes. Concretely, we propose an unsupervised prototype discovery and refining strategy to obtain prototypes that can precisely reconstruct the classification head, yet being interpretable. Besides guaranteeing the performance, we show that our network offers more faithful explanations qualitatively and yields even better part-prototypes quantitatively than prior part-prototype networks.} }
Endnote
%0 Conference Paper %T Post-hoc Part-Prototype Networks %A Andong Tan %A Fengtao Zhou %A Hao Chen %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-tan24g %I PMLR %P 47681--47693 %U https://proceedings.mlr.press/v235/tan24g.html %V 235 %X Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal ”where” a model looks at for a given input, fail to explain ”what” the model looks for (e.g., what is important to classify a bird image to a Scott Oriole?). Existing part-prototype networks leverage part-prototypes (e.g., characteristic Scott Oriole’s wing and head) to answer both ”where" and ”what", but often under-perform their black box counterparts in the accuracy. Therefore, a natural question is: can one construct a network that answers both ”where” and ”what" in a post-hoc manner to guarantee the model’s performance? To this end, we propose the first post-hoc part-prototype network via decomposing the classification head of a trained model into a set of interpretable part-prototypes. Concretely, we propose an unsupervised prototype discovery and refining strategy to obtain prototypes that can precisely reconstruct the classification head, yet being interpretable. Besides guaranteeing the performance, we show that our network offers more faithful explanations qualitatively and yields even better part-prototypes quantitatively than prior part-prototype networks.
APA
Tan, A., Zhou, F. & Chen, H.. (2024). Post-hoc Part-Prototype Networks. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:47681-47693 Available from https://proceedings.mlr.press/v235/tan24g.html.

Related Material