Explaining an image classifier with a generative model conditioned by uncertainty
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912105247014912 |
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| author | LeCoz, Adrien Herbin, Stéphane Adjed, Faouzi |
| author_facet | LeCoz, Adrien Herbin, Stéphane Adjed, Faouzi |
| contents | We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a corrupted version of MNIST dataset illustrate the idea. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explaining an image classifier with a generative model conditioned by uncertainty LeCoz, Adrien Herbin, Stéphane Adjed, Faouzi Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a corrupted version of MNIST dataset illustrate the idea. |
| title | Explaining an image classifier with a generative model conditioned by uncertainty |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2410.13871 |