PixOOD: Pixel-Level Out-of-Distribution Detection
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916451331342336 |
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| author | Vojíř, Tomáš Šochman, Jan Matas, Jiří |
| author_facet | Vojíř, Tomáš Šochman, Jan Matas, Jiří |
| contents | We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PixOOD: Pixel-Level Out-of-Distribution Detection Vojíř, Tomáš Šochman, Jan Matas, Jiří Computer Vision and Pattern Recognition We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD. |
| title | PixOOD: Pixel-Level Out-of-Distribution Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19882 |