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arXiv · 2609.14097

Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification

Siddhant Bharadwaj·Ashish Vashist·Rashi Singh·Pranav Vinodh·Nishanth Artham·Runmin Jiang·Xingjian Li·Min Xu
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Abstract

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.

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Siddhant Bharadwaj, Ashish Vashist, Rashi Singh, Pranav Vinodh, Nishanth Artham, Runmin Jiang, Xingjian Li, Min Xu. 2026-09-12. Bridging the Synthetic-to-Real Gap for Few-Shot Cryo-ET Classification. https://arxiv.org/abs/2609.14097

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