# Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation

arXiv: https://arxiv.org/abs/2001.08735 · HTML: https://mlsanity.com/paper/2001.08735/

## Key facts

- Cited by 185 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 30 of 185)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Regularizing Generative Adversarial Networks under Limited Data](https://mlsanity.com/paper/2104.03310/) (2021)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Cross-Domain Few-Shot Classification via Adversarial Task Augmentation](https://mlsanity.com/paper/2104.14385/) (2021)
- [Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency](https://mlsanity.com/paper/2107.11355/) (2021)
- [Learning a Universal Template for Few-shot Dataset Generalization](https://mlsanity.com/paper/2105.07029/) (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)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification](https://mlsanity.com/paper/2204.04567/) (2022)
- [Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition](https://mlsanity.com/paper/2101.02833/) (2021)
- [Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data](https://mlsanity.com/paper/2107.11978/) (2021)
- [Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector](https://mlsanity.com/paper/2402.03094/) (2024)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty](https://mlsanity.com/paper/2202.01339/) (2022)
- [Adversarial Feature Augmentation for Cross-domain Few-shot Classification](https://mlsanity.com/paper/2208.11021/) (2022)
- [Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining](https://mlsanity.com/paper/2401.08407/) (2024)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Ranking Distance Calibration for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2112.00260/) (2021)
- [Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.00567/) (2024)
- [Matching Feature Sets for Few-Shot Image Classification](https://mlsanity.com/paper/2204.00949/) (2022)
- [Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning](https://mlsanity.com/paper/2210.07805/) (2022)
- [NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results](https://mlsanity.com/paper/2504.10685/) (2025)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2203.07656/) (2022)
- [How to Train Your MAML to Excel in Few-Shot Classification](https://mlsanity.com/paper/2106.16245/) (2021)
- [Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation](https://mlsanity.com/paper/2402.17614/) (2024)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Channel Importance Matters in Few-Shot Image Classification](https://mlsanity.com/paper/2206.08126/) (2022)
- [Cross-Domain Few-Shot Graph Classification](https://mlsanity.com/paper/2201.08265/) (2022)

## Abstract (arXiv)

Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories by comparing the feature embeddings of query images with those from a few labeled images (support examples) using a learned metric function. While promising performance has been demonstrated, these methods often fail to generalize to unseen domains due to large discrepancy of the feature distribution across domains. In this work, we address the problem of few-shot classification under domain shifts for metric-based methods. Our core idea is to use feature-wise transformation layers for augmenting the image features using affine transforms to simulate various feature distributions under different domains in the training stage. To capture variations of the feature distributions under different domains, we further apply a learning-to-learn approach to search for the hyper-parameters of the feature-wise transformation layers. We conduct extensive experiments and ablation studies under the domain generalization setting using five few-shot classification datasets: mini-ImageNet, CUB, Cars, Places, and Plantae. Experimental results demonstrate that the proposed feature-wise transformation layer is applicable to various metric-based models, and provides consistent improvements on the few-shot classification performance under domain shift.