Dynamic self-ensemble on multi-exit residual vision transformer for small-data image classification
Vision Transformer (ViT) has garnered increased attention for image classification in recent years. However, ViT is notorious for its data-hungry problem as it achieves high-performance results when only trained on large-size datasets. In this paper, we study how ViT specifically works on small-scale datasets with limited data where the availability of image data is often constrained in practice, especially in fields such as medical imaging. To mitigate the issue, we designed a multi-stage residual ViT, dubbed ReSEViT, with the self-ensemble method to utilize multi-level information from intermediate features. Due to the complex decision boundaries for classifications, relying on a single...
| Authors | Stephen Ekaputra Limantoro, Yan-Kai Wang, Ching-Chun Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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