ComFe: Interpretable Image Classifiers With Foundation Models

• URL: http://arxiv.org/abs/2403.04125v4
• Date: Fri, 22 Nov 2024 01:41:20 GMT
• Title: ComFe: Interpretable Image Classifiers With Foundation Models
• Authors: Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell,
• Abstract summary: Interpretable computer vision models explain their classifications through comparing distances between the embeddings of an image and a set of prototypes that represent the training data. ComFe is the first interpretable approach that can be applied at the scale of such datasets as ImageNet-1K. ComFe can identify consistent component features within an image and determine which of these features are informative in making a prediction.
• Score: 8.572967695281054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent the training data. However, these approaches introduce additional hyper-parameters that need to be tuned to apply to new datasets, scale poorly, and are more computationally intensive to train in comparison to black-box approaches. In this work, we introduce Component Features (ComFe), a modular and highly scalable interpretable-by-design image classification approach for pretrained Vision Transformers (ViTs) that can obtain competitive performance in comparison to comparable non-interpretable methods. ComFe is the first interpretable approach, that we know of, that can be applied at the scale of datasets such as ImageNet-1K. Additionally, ComFe provides improved robustness over non-interpretable methods and outperforms previous interpretable approaches on key benchmark datasets $\unicode{x2013}$ using a consistent set of hyper-parameters and without finetuning the pretrained ViT backbone. With only global image labels and no segmentation or part annotations, ComFe can identify consistent component features within an image and determine which of these features are informative in making a prediction.

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