# Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

arXiv: https://arxiv.org/abs/2005.08463 · HTML: https://mlsanity.com/paper/2005.08463/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 20 of 20)

- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Cross-Domain Few-Shot Classification via Adversarial Task Augmentation](https://mlsanity.com/paper/2104.14385/) (2021)
- [StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2302.09309/) (2023)
- [Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder](https://mlsanity.com/paper/2108.05028/) (2021)
- [Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data](https://mlsanity.com/paper/2107.11978/) (2021)
- [Ranking Distance Calibration for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2112.00260/) (2021)
- [Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2203.07656/) (2022)
- [Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark](https://mlsanity.com/paper/2104.02638/) (2021)
- [Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning](https://mlsanity.com/paper/2306.10511/) (2023)
- [Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation](https://mlsanity.com/paper/2404.10322/) (2024)
- [Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.01966/) (2024)
- [Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting](https://mlsanity.com/paper/2412.00767/) (2024)
- [Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning](https://mlsanity.com/paper/2411.10070/) (2024)
- [SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2412.09073/) (2024)
- [Cross-Domain Few-Shot Classification via Inter-Source Stylization](https://mlsanity.com/paper/2208.08015/) (2022)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)
- [A Survey of Deep Visual Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2303.09253/) (2023)
- [Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts](https://mlsanity.com/paper/2505.15506/) (2025)
- [Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2511.14279/) (2025)
- [Domain Agnostic Few-Shot Learning For Document Intelligence](https://mlsanity.com/paper/2111.00007/) (2021)

## Abstract (arXiv)

In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we proposes to construct an ensemble prediction model by performing diverse feature transformations after a feature extraction network. On each branch prediction network of the model we use a batch spectral regularization term to suppress the singular values of the feature matrix during pre-training to improve the generalization ability of the model. The proposed model can then be fine tuned in the target domain to address few-shot classification. We also further apply label propagation, entropy minimization and data augmentation to mitigate the shortage of labeled data in target domains. Experiments are conducted on a number of CD-FSL benchmark tasks with four target domains and the results demonstrate the superiority of our proposed model.