TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910430695260160 |
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| author | Taghanaki, Saeid Asgari Khani, Aliasghar Pasand, Ali Saheb Khasahmadi, Amir Sanghi, Aditya Willis, Karl D. D. Mahdavi-Amiri, Ali |
| author_facet | Taghanaki, Saeid Asgari Khani, Aliasghar Pasand, Ali Saheb Khasahmadi, Amir Sanghi, Aditya Willis, Karl D. D. Mahdavi-Amiri, Ali |
| contents | Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, we propose a novel method that leverages the capabilities of language models to interpret the learned features of pre-trained image classifiers. Our method, called TExplain, tackles this task by training a neural network to establish a connection between the feature space of image classifiers and language models. Then, during inference, our approach generates a vast number of sentences to explain the features learned by the classifier for a given image. These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier. Our method, for the first time, utilizes these frequent words corresponding to a visual representation to provide insights into the decision-making process of the independently trained classifier, enabling the detection of spurious correlations, biases, and a deeper comprehension of its behavior. To validate the effectiveness of our approach, we conduct experiments on diverse datasets, including ImageNet-9L and Waterbirds. The results demonstrate the potential of our method to enhance the interpretability and robustness of image classifiers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_00733 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models Taghanaki, Saeid Asgari Khani, Aliasghar Pasand, Ali Saheb Khasahmadi, Amir Sanghi, Aditya Willis, Karl D. D. Mahdavi-Amiri, Ali Computer Vision and Pattern Recognition Machine Learning Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, we propose a novel method that leverages the capabilities of language models to interpret the learned features of pre-trained image classifiers. Our method, called TExplain, tackles this task by training a neural network to establish a connection between the feature space of image classifiers and language models. Then, during inference, our approach generates a vast number of sentences to explain the features learned by the classifier for a given image. These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier. Our method, for the first time, utilizes these frequent words corresponding to a visual representation to provide insights into the decision-making process of the independently trained classifier, enabling the detection of spurious correlations, biases, and a deeper comprehension of its behavior. To validate the effectiveness of our approach, we conduct experiments on diverse datasets, including ImageNet-9L and Waterbirds. The results demonstrate the potential of our method to enhance the interpretability and robustness of image classifiers. |
| title | TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2309.00733 |