Understanding Test-Time Augmentation
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917587027230720 |
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| author | Kimura, Masanari |
| author_facet | Kimura, Masanari |
| contents | Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectiveness of TTA, there is insufficient discussion of its theoretical aspects. In this paper, we aim to give theoretical guarantees for TTA and clarify its behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Test-Time Augmentation Kimura, Masanari Machine Learning Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectiveness of TTA, there is insufficient discussion of its theoretical aspects. In this paper, we aim to give theoretical guarantees for TTA and clarify its behavior. |
| title | Understanding Test-Time Augmentation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.06892 |