Hidden in Plain Sight -- Class Competition Focuses Attribution Maps
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918324085981184 |
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| author | Walter, Nils Philipp Vreeken, Jilles Fischer, Jonas |
| author_facet | Walter, Nils Philipp Vreeken, Jilles Fischer, Jonas |
| contents | Attribution methods reveal which input features a neural network uses for a prediction, adding transparency to their decisions. A common problem is that these attributions seem unspecific, highlighting both important and irrelevant features. We revisit the common attribution pipeline and observe that using logits as attribution target is a main cause of this phenomenon. We show that the solution is in plain sight: considering distributions of attributions over multiple classes using existing attribution methods yields specific and fine-grained attributions. On common benchmarks, including the grid-pointing game and randomization-based sanity checks, this improves the ability of 18 attribution methods across 7 architectures up to 2x, agnostic to model architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hidden in Plain Sight -- Class Competition Focuses Attribution Maps Walter, Nils Philipp Vreeken, Jilles Fischer, Jonas Computer Vision and Pattern Recognition Machine Learning Attribution methods reveal which input features a neural network uses for a prediction, adding transparency to their decisions. A common problem is that these attributions seem unspecific, highlighting both important and irrelevant features. We revisit the common attribution pipeline and observe that using logits as attribution target is a main cause of this phenomenon. We show that the solution is in plain sight: considering distributions of attributions over multiple classes using existing attribution methods yields specific and fine-grained attributions. On common benchmarks, including the grid-pointing game and randomization-based sanity checks, this improves the ability of 18 attribution methods across 7 architectures up to 2x, agnostic to model architecture. |
| title | Hidden in Plain Sight -- Class Competition Focuses Attribution Maps |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.07346 |