# A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping

arXiv: https://arxiv.org/abs/2504.06608 · HTML: https://mlsanity.com/paper/2504.06608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Prevalence of Neural Collapse during the terminal phase of deep learning training](https://mlsanity.com/paper/2008.08186/) (2020)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation](https://mlsanity.com/paper/2001.08735/) (2020)
- [Self-training for Few-shot Transfer Across Extreme Task Differences](https://mlsanity.com/paper/2010.07734/) (2020)
- [Cross-Domain Few-Shot Classification via Adversarial Task Augmentation](https://mlsanity.com/paper/2104.14385/) (2021)
- [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 Learning by Representation Fusion](https://mlsanity.com/paper/2010.06498/) (2020)
- [Ranking Distance Calibration for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2112.00260/) (2021)
- [Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning](https://mlsanity.com/paper/2306.10511/) (2023)
- [Hierarchical Variational Memory for Few-shot Learning Across Domains](https://mlsanity.com/paper/2112.08181/) (2021)
- [CDFSL-V: Cross-Domain Few-Shot Learning for Videos](https://mlsanity.com/paper/2309.03989/) (2023)
- [Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition](https://mlsanity.com/paper/2008.03128/) (2020)
- [Cross-domain few-shot learning with unlabelled data](https://mlsanity.com/paper/2101.07899/) (2021)
- [Cross-Domain Few-Shot Learning via Adaptive Transformer Networks](https://mlsanity.com/paper/2401.13987/) (2024)
- [Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations](https://mlsanity.com/paper/2207.07826/) (2022)

## Abstract (arXiv)

In task-based few-shot learning paradigms, it is commonly assumed that different tasks are independently and identically distributed (i.i.d.). However, in real-world scenarios, the distribution encountered in few-shot learning can significantly differ from the distribution of existing data. Thus, how to effectively leverage existing data knowledge to enable models to quickly adapt to class variations under non-i.i.d. assumptions has emerged as a key research challenge. To address this challenge, this paper proposes a new cross-domain few-shot learning approach based on domain knowledge mapping, applied consistently throughout the pre-training, training, and testing phases. In the pre-training phase, our method integrates self-supervised and supervised losses by maximizing mutual information, thereby mitigating mode collapse. During the training phase, the domain knowledge mapping layer collaborates with a domain classifier to learn both domain mapping capabilities and the ability to assess domain adaptation difficulty. Finally, this approach is applied during the testing phase, rapidly adapting to domain variations through meta-training tasks on support sets, consequently enhancing the model's capability to transfer domain knowledge effectively. Experimental validation conducted across six datasets from diverse domains demonstrates the effectiveness of the proposed method.