Forward Learning for Gradient-based Black-box Saliency Map Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929405306077184 |
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| author | Zhang, Zeliang Feng, Mingqian Jiang, Jinyang Zhu, Rongyi Peng, Yijie Xu, Chenliang |
| author_facet | Zhang, Zeliang Feng, Mingqian Jiang, Jinyang Zhu, Rongyi Peng, Yijie Xu, Chenliang |
| contents | Gradient-based saliency maps are widely used to explain deep neural network decisions. However, as models become deeper and more black-box, such as in closed-source APIs like ChatGPT, computing gradients become challenging, hindering conventional explanation methods. In this work, we introduce a novel unified framework for estimating gradients in black-box settings and generating saliency maps to interpret model decisions. We employ the likelihood ratio method to estimate output-to-input gradients and utilize them for saliency map generation. Additionally, we propose blockwise computation techniques to enhance estimation accuracy. Extensive experiments in black-box settings validate the effectiveness of our method, demonstrating accurate gradient estimation and explainability of generated saliency maps. Furthermore, we showcase the scalability of our approach by applying it to explain GPT-Vision, revealing the continued relevance of gradient-based explanation methods in the era of large, closed-source, and black-box models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forward Learning for Gradient-based Black-box Saliency Map Generation Zhang, Zeliang Feng, Mingqian Jiang, Jinyang Zhu, Rongyi Peng, Yijie Xu, Chenliang Computer Vision and Pattern Recognition Artificial Intelligence Gradient-based saliency maps are widely used to explain deep neural network decisions. However, as models become deeper and more black-box, such as in closed-source APIs like ChatGPT, computing gradients become challenging, hindering conventional explanation methods. In this work, we introduce a novel unified framework for estimating gradients in black-box settings and generating saliency maps to interpret model decisions. We employ the likelihood ratio method to estimate output-to-input gradients and utilize them for saliency map generation. Additionally, we propose blockwise computation techniques to enhance estimation accuracy. Extensive experiments in black-box settings validate the effectiveness of our method, demonstrating accurate gradient estimation and explainability of generated saliency maps. Furthermore, we showcase the scalability of our approach by applying it to explain GPT-Vision, revealing the continued relevance of gradient-based explanation methods in the era of large, closed-source, and black-box models. |
| title | Forward Learning for Gradient-based Black-box Saliency Map Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.15603 |