Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data (2107.11978v1)

Abstract: A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motivates the task of Cross-Domain Few-Shot Learning (CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has not been leveraged in any way to help the learning process. Thus, we advocate utilizing few labeled target data to guide the model learning. Technically, a novel meta-FDMixup network is proposed. We tackle this problem mainly from two aspects. Firstly, to utilize the source and the newly introduced target data of two different class sets, a mixup module is re-proposed and integrated into the meta-learning mechanism. Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features. These two modules together enable our model to narrow the domain gap thus generalizing well to the target datasets. Additionally, a detailed feasibility and pilot study is conducted to reflect the intuitive understanding of CD-FSL under our new setting. Experimental results show the effectiveness of our new setting and the proposed method. Codes and models are available at https://github.com/lovelyqian/Meta-FDMixup.

• The paper introduces the innovative meta-FDMixup network that combines minimal labeled target data with mixup and disentangle modules to bridge domain gaps.
• It leverages a mixup module to synthesize diverse training examples and a disentangle module to extract domain-irrelevant features for improved few-shot classification.
• Extensive experiments on datasets like Mini-Imagenet, CUB, and Cars demonstrate that the approach significantly enhances cross-domain model accuracy.

Overview of "Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data"

The paper "Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data" addresses a significant challenge in the domain of cross-domain few-shot learning (CD-FSL). Specifically, the research confronts the problem wherein few-shot learning models, trained on a source domain, fail to generalize effectively to a novel, target domain when a domain shift is observed. The authors propose leveraging a minimal amount of labeled target data during the training process, a strategy that was previously not exploited, to improve the generalization capabilities of few-shot learning models across domains.

Key Contributions

1. Introduction of Target Data in Training: The paper introduces a novel approach of using a small subset of labeled target data to guide the meta-learning process in CD-FSL. This auxiliary dataset functions as an additional supervisory signal to narrow the domain gap, a gap which, when too large, undermines the generalization performance of existing models.
2. Meta-FDMixup Network: To operationalize the introduced labeled target data, the authors propose the meta-FDMixup network. This model incorporates two main components: a mixup module and a disentangle module. The mixup module synthesizes new training samples by mixing source and target domain examples, providing a diversified training set that spans across domains. Meanwhile, the disentangle module is designed to separate domain-irrelevant features from domain-specific features, thus allowing the model to focus only on the most relevant information for few-shot classification tasks.
3. Feasibility Analysis and Training Strategy: The research provides a detailed pilot paper and feasibility analysis to illustrate how and when introducing auxiliary data during the two-step training (pretraining followed by meta-training) positively impacts performance. Specifically, training stage influences and varying levels of labeled target data are analyzed to optimize the training process.
4. Experimental Validation: Extensive empirical evaluations demonstrate the efficacy of the proposed method. The authors conduct tests on several datasets, including Mini-Imagenet, CUB, Cars, Places, and Plantae. The results indicate substantial improvements in model accuracy on target domains when the meta-FDMixup network is used compared to traditional few-shot learning methods and baseline CD-FSL approaches.

Implications and Future Directions

The findings from this paper contribute to both theoretical and practical advancements in few-shot learning and cross-domain adaptation. By showing that a limited amount of target data can be instrumental in reducing domain shift, the research opens new avenues for developing practical models in data-scarce environments. This has significant implications for applications like medical image analysis and other fields where labeled data acquisition is expensive or infeasible.

Future research can expand upon this work by exploring adaptive methods to dynamically determine the optimal number of auxiliary examples for different tasks, potentially incorporating more sophisticated data selection or augmentation strategies. Moreover, further investigation into the disentanglement mechanisms may yield insights into improving domain-generalizable representations, particularly when scaling up to more complex domain shifts or class imbalances.

Conclusion

The paper presents a significant advancement in the CD-FSL domain, effectively leveraging minimal labeled target data to guide model adaptation across domain shifts. The proposed meta-FDMixup method, with its innovative mixup and disentanglement approach, demonstrates strong results and sets a foundation for future exploration and refinement in cross-domain few-shot learning strategies.