Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909357784956928 |
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| author | Boyd, Aidan Trabelsi, Mohamed Uzunalioglu, Huseyin Kushnir, Dan |
| author_facet | Boyd, Aidan Trabelsi, Mohamed Uzunalioglu, Huseyin Kushnir, Dan |
| contents | Understanding specifically where a model focuses on within an image is critical for human interpretability of the decision-making process. Deep learning-based solutions are prone to learning coincidental correlations in training datasets, causing over-fitting and reducing the explainability. Recent advances have shown that guiding models to human-defined regions of saliency within individual images significantly increases performance and interpretability. Human-guided models also exhibit greater generalization capabilities, as coincidental dataset features are avoided. Results show that models trained with saliency incorporation display an increase in interpretability of up to 30% over models trained without saliency information. The collection of this saliency information, however, can be costly, laborious and in some cases infeasible. To address this limitation, we propose a combination strategy of saliency incorporation and active learning to reduce the human annotation data required by 80% while maintaining the interpretability and performance increase from human saliency. Extensive experimentation outlines the effectiveness of the proposed approach across five public datasets and six active learning criteria. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16115 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency Boyd, Aidan Trabelsi, Mohamed Uzunalioglu, Huseyin Kushnir, Dan Computer Vision and Pattern Recognition Understanding specifically where a model focuses on within an image is critical for human interpretability of the decision-making process. Deep learning-based solutions are prone to learning coincidental correlations in training datasets, causing over-fitting and reducing the explainability. Recent advances have shown that guiding models to human-defined regions of saliency within individual images significantly increases performance and interpretability. Human-guided models also exhibit greater generalization capabilities, as coincidental dataset features are avoided. Results show that models trained with saliency incorporation display an increase in interpretability of up to 30% over models trained without saliency information. The collection of this saliency information, however, can be costly, laborious and in some cases infeasible. To address this limitation, we propose a combination strategy of saliency incorporation and active learning to reduce the human annotation data required by 80% while maintaining the interpretability and performance increase from human saliency. Extensive experimentation outlines the effectiveness of the proposed approach across five public datasets and six active learning criteria. |
| title | Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.16115 |