A Broader Study of Cross-Domain Few-Shot Learning (1912.07200v3)

Abstract: Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains. While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity. No work yet exists that examines few-shot learning across different imaging methods seen in real world scenarios, such as aerial and medical imaging. In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods. This includes natural images, such as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning. The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. Performance gains previously observed with methods specialized for cross-domain few-shot learning vanish in this more challenging benchmark. Finally, accuracy of all methods tend to correlate with dataset similarity to natural images, verifying the value of the benchmark to better represent the diversity of data seen in practice and guiding future research.

An In-Depth Examination of Cross-Domain Few-Shot Learning

The paper "A Broader Study of Cross-Domain Few-Shot Learning" addresses a critical challenge in machine learning: learning from few examples across different domains. Traditional few-shot learning methods, which generally rely on the similarity between base and novel classes within the same domain, face significant hurdles when applied to cross-domain scenarios. To investigate this, the authors propose a comprehensive benchmark called the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL).

Overview of the BSCD-FSL Benchmark

The BSCD-FSL benchmark is designed to assess the robustness of few-shot learning methods across drastically different image domains. This includes data from agriculture (CropDiseases), satellite (EuroSAT), dermatological (ISIC2018), and radiological (ChestX) images, which vary widely in their similarity to natural images such as those in ImageNet. The benchmark evaluates the impact of three orthogonal criteria: perspective distortion, semantic content, and color depth.

Key Findings and Experimental Results

The paper conducts extensive evaluations using state-of-the-art meta-learning techniques, adapted to this challenging cross-domain context. Surprisingly, the results reveal that traditional meta-learning methods are outperformed by simpler transfer learning approaches like fine-tuning. In some instances, meta-learning algorithms even underperform when compared to networks initialized with random weights—a rather unexpected finding that questions the prior efficacy of meta-learning advancements in cross-domain applications.

The results also show that specific methods tailored for cross-domain few-shot learning, such as Feature-Wise Transform (FWT), do not enhance performance and sometimes degrade it. This outcome is critical as it emphasizes the necessity for reassessment of meta-learning methods when facing cross-domain challenges. The correlation between the accuracy of methods and the dataset similarity to ImageNet further validates the BSCD-FSL benchmark’s construct.

Implications and Future Directions

The findings suggest significant implications for designing few-shot learning models that are robust across domains. The reversal of performance superiority, with standard fine-tuning outperforming sophisticated meta-learning methods, indicates a potential paradigm shift in few-shot learning research for cross-domain applications. The research underscores the importance of developing models that can successfully generalize across diverse domain shifts.

Looking forward, this paper opens new avenues for exploration. The poor performance of meta-learning methods in this more challenging setting inspires future research to focus on creating approaches that can bridge the domain gap more effectively. It also emphasizes the need for further investigation into the design of training strategies and architectures that exploit the inherent characteristics of target domains and can leverage limited data under significant domain shifts.

Conclusion

In summary, the paper presents a thorough investigation into cross-domain few-shot learning, challenging prevailing assumptions about the applicability of meta-learning methods in such settings. The BSCD-FSL benchmark emerges as a valuable tool for guiding future research, promoting the development of techniques that can effectively handle few-shot learning scenarios in real-world, diverse applications. This work not only highlights the current limitations but also paves the way for impactful innovations in cross-domain learning strategies.