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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.13989 |
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| _version_ | 1866910656148537344 |
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| author | Fletcher, Luan van der Klis, Robert Sedláček, Martin Vasilev, Stefan Athanasiadis, Christos |
| author_facet | Fletcher, Luan van der Klis, Robert Sedláček, Martin Vasilev, Stefan Athanasiadis, Christos |
| contents | The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language supervision from a vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led to improved classification performance or improvements in quantitative and qualitative measures of interpretability. Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13989 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reproducibility study of "LICO: Explainable Models with Language-Image Consistency" Fletcher, Luan van der Klis, Robert Sedláček, Martin Vasilev, Stefan Athanasiadis, Christos Computer Vision and Pattern Recognition The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language supervision from a vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led to improved classification performance or improvements in quantitative and qualitative measures of interpretability. Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research. |
| title | Reproducibility study of "LICO: Explainable Models with Language-Image Consistency" |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.13989 |