Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification
Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulators can be adopted to generate unlimited synthetic data, the substantial domain gap between synthetic and real subtomograms hinders its practical utilization. In this work, we propose a novel synthetic-to-real adaptation framework with a learnable transformation module, bridging this gap at both the input and feature levels. Extensive experiments demonstrate that our method consistently outperforms existing transfer learning baselines in few-shot settings.
| Authors | Siddhant Bharadwaj, Ashish Vashist, Rashi Singh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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