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

arXiv: https://arxiv.org/abs/2609.14097 · HTML: https://mlsanity.com/paper/2609.14097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning](https://mlsanity.com/paper/1702.08690/) (2017)
- [Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning](https://mlsanity.com/paper/2402.15017/) (2024)
- [Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms](https://mlsanity.com/paper/2007.15422/) (2020)
- [Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization](https://mlsanity.com/paper/2111.09114/) (2021)

## Abstract (arXiv)

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.