Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder (2108.05028v2)

Abstract: State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets. The strong discrimination ability on the source dataset does not necessarily translate to high classification accuracy on the target dataset. In this work, we address this cross-domain few-shot learning (CDFSL) problem by boosting the generalization capability of the model. Specifically, we teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE). NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. Theoretical analysis based on intra-class correlation (ICC) shows that the feature embeddings learned from NSAE have stronger discrimination and generalization abilities in the target domain. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain. Extensive experiments and ablation studies are conducted to demonstrate the effectiveness of the proposed method. Experimental results show that our proposed method consistently outperforms SOTA methods under various conditions.

• The paper introduces a noise-enhanced supervised autoencoder (NSAE) that improves feature discrimination in cross-domain few-shot learning.
• It employs a two-step fine-tuning process that first adapts the model to the target domain through input reconstruction and then refines classification performance.
• Extensive experiments demonstrate that NSAE consistently overcomes domain shift challenges, achieving robust performance across unseen domains.

Boosting Generalization in Cross-Domain Few-shot Learning

Few-shot learning (FSL) methods traditionally excel in scenarios where large-scale datasets are available for training. However, the extension of these methods to cross-domain few-shot learning (CDFSL), where source and target datasets are highly dissimilar, presents additional challenges. The paper "Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder" introduces a strategy to enhance model generalization across domains through the novel application of noise-enhanced supervised autoencoders (NSAE).

Overview

The paper begins by identifying the limitations of state-of-the-art few-shot learning techniques when adapted to cross-domain contexts. Typically, models trained on a source domain fail to maintain high classification accuracy when applied to a target domain with distinct feature distribution. The authors address this gap by proposing an innovative approach that enhances the model's ability to generalize beyond its training domain, leveraging supervised autoencoders augmented with inherent noise.

Methodology

A critical component of this methodology is the introduction of the noise-enhanced supervised autoencoder (NSAE). This model operates by reconstructing inputs and simultaneously predicting labels from both the inputs and their reconstructed versions. Such a design is underpinned by theoretical analysis demonstrating that feature embeddings learned this way exhibit robust discrimination and generalization abilities.

The NSAE notably incorporates a two-step fine-tuning stage. Initially, the model undergoes unsupervised adaptation to the target domain by reconstructing images, which effectively reduces the domain shift impact. Finely-tuned as a classification model thereafter, this approach provides remarkable improvement in classification performance.

Analytical and Experimental Results

Through extensive experiments and ablation studies across various benchmark datasets, the NSAE consistently surpassed traditional FSL methodologies in overcoming domain shifts. These experiments employed varying model architectures and loss functions, confirming the robustness and generality of the proposed solution. Notably, under ablation studies, NSAE demonstrated substantial improvement in performance across multiple unseen domains with significant domain shifts, underscoring the model's effectiveness in enhancing generalization.

Implications and Future Directions

The successful implementation of NSAE in cross-domain contexts questions previous assumptions about negative transfer typically seen in FSL when domains diverge. By capturing variance in feature distributions effectively, NSAE paves a potential pathway for other machine learning frameworks to explore novel generalization techniques.

The results of this research indicate broad applications in fields requiring model adaptation across disparate contexts, such as medical image classification where data from different modalities might need harmonization. Future work might investigate deeper integration mechanisms within NSAE or explore its utility in generative contexts, given its reconstruction-based approach.

Conclusion

The paper provides a methodical advancement in addressing the complexities of cross-domain few-shot learning by harnessing a noise-enhanced supervised autoencoder framework. By enabling models to generalize more robustly across domains with no exhaustive retraining required, this research contributes significantly to evolving FSL methodologies. The implications of this work extend both practical applications and theoretical understandings of domain adaptation in machine learning.