A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Paper • 1910.04867 • Published
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VTAB PatchCamelyon
This dataset has been used for the paper Fantastic Features and Where to Find Them: A Probing Method to combine Features from Multiple Foundation Models (NeurIPS 2025).
It reproduces the settings (splits, labels) used for the Visual Task Adaptation Benchmark (VTAB).
- VTAB Paper: A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
- VTAB Repository: google-research/task_adaptation
Details of the original dataset:
- Original Citations:
- PatchCamelyon: B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962.
- Original Camelyon16 dataset : Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585.
- Original Homepage: PatchCamelyon GitHub
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