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Interpreting the learned features of vision models has posed a longstandingchallenge in the field of machine learning. To address this issue, we propose anovel method that leverages the capabilities of language models to interpretthe learned features of pre-trained image classifiers. Our method, calledTExplain, tackles this task by training a neural network to establish aconnection between the feature space of image classifiers and language models.Then, during inference, our approach generates a vast number of sentences toexplain the features learned by the classifier for a given image. Thesesentences are then used to extract the most frequent words, providing acomprehensive understanding of the learned features and patterns within theclassifier. Our method, for the first time, utilizes these frequent wordscorresponding to a visual representation to provide insights into thedecision-making process of the independently trained classifier, enabling thedetection of spurious correlations, biases, and a deeper comprehension of itsbehavior. To validate the effectiveness of our approach, we conduct experimentson diverse datasets, including ImageNet-9L and Waterbirds. The resultsdemonstrate the potential of our method to enhance the interpretability androbustness of image classifiers.

TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models