Enhancing Robustness in Long-Tailed Image Classification with Angular Approaches and Balanced Learning
Abstract When the data distribution in a dataset is highly imbalanced or long-tailed, this can lead to biased deep network models that are ineffective at handling samples from the tail classes. The issue arises because the features of the samples in the head classes are dominant during the learning...
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| Auteurs principaux: | , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
SpringerOpen
2025-05-01
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| Collection: | Data Science and Engineering |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/s41019-025-00286-x |
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