A Learning Paradigm for Interpretable Gradients
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909178883211264 |
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| author | Figueroa, Felipe Torres Zhang, Hanwei Sicre, Ronan Avrithis, Yannis Ayache, Stephane |
| author_facet | Figueroa, Felipe Torres Zhang, Hanwei Sicre, Ronan Avrithis, Yannis Ayache, Stephane |
| contents | This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, it is well understood that gradients are noisy and alternatives like guided backpropagation have been proposed to obtain better visualization at inference. In this work, we present a novel training approach to improve the quality of gradients for interpretability. In particular, we introduce a regularization loss such that the gradient with respect to the input image obtained by standard backpropagation is similar to the gradient obtained by guided backpropagation. We find that the resulting gradient is qualitatively less noisy and improves quantitatively the interpretability properties of different networks, using several interpretability methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Learning Paradigm for Interpretable Gradients Figueroa, Felipe Torres Zhang, Hanwei Sicre, Ronan Avrithis, Yannis Ayache, Stephane Computer Vision and Pattern Recognition Machine Learning This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, it is well understood that gradients are noisy and alternatives like guided backpropagation have been proposed to obtain better visualization at inference. In this work, we present a novel training approach to improve the quality of gradients for interpretability. In particular, we introduce a regularization loss such that the gradient with respect to the input image obtained by standard backpropagation is similar to the gradient obtained by guided backpropagation. We find that the resulting gradient is qualitatively less noisy and improves quantitatively the interpretability properties of different networks, using several interpretability methods. |
| title | A Learning Paradigm for Interpretable Gradients |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.15024 |