Adversarial Feature Hallucination Networks for Few-Shot Learning (2003.13193v2)

Abstract: The recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL), which aims to learn concept of new classes with a few labeled samples. A natural approach to FSL is data augmentation and many recent works have proved the feasibility by proposing various data synthesis models. However, these models fail to well secure the discriminability and diversity of the synthesized data and thus often produce undesirable results. In this paper, we propose Adversarial Feature Hallucination Networks (AFHN) which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples. Two novel regularizers, i.e., the classification regularizer and the anti-collapse regularizer, are incorporated into AFHN to encourage discriminability and diversity of the synthesized features, respectively. Ablation study verifies the effectiveness of the proposed cWGAN based feature hallucination framework and the proposed regularizers. Comparative results on three common benchmark datasets substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones.

• The paper introduces adversarial feature hallucination networks leveraging cWGANs to synthesize discriminative and diverse features for few-shot learning.
• The method incorporates two novel regularizers—classification and anti-collapse—to enhance feature alignment and prevent mode collapse.
• Empirical evaluations on Mini-ImageNet, CUB, and CIFAR100 demonstrate state-of-the-art improvements in both 1-shot and 5-shot classification tasks.

An Examination of Adversarial Feature Hallucination Networks for Few-Shot Learning

Few-shot learning (FSL) has become an increasingly significant focus within the machine learning community, primarily due to its ability to leverage minimal labeled data to recognize new classes. This capability directly addresses the challenges posed by real-world applications where extensive labeled datasets are often unavailable. The paper "Adversarial Feature Hallucination Networks for Few-Shot Learning" introduces a novel approach employing adversarial networks to effectively augment and enhance FSL's performance.

Conceptual Framework and Methodology

The authors propose the Adversarial Feature Hallucination Networks (AFHN), leveraging conditional Wasserstein Generative Adversarial Networks (cWGANs). This approach is notably distinct from other data augmentation methods that typically synthesize features in image space. Instead, AFHN synthesizes features in the feature space using a small set of labeled samples as the conditional input. This is crucial as it directly addresses the dual challenges of ensuring both discriminability and diversity in synthesized features, which are essential for creating robust classification models.

Key innovations in AFHN include the introduction of two novel regularizers:

1. Classification Regularizer: This regularizer promotes discriminability by encouraging synthesized features to align closely with real features within the same class while diverging from those of different classes.
2. Anti-Collapse Regularizer: This component tackles the notorious mode collapse issue in GANs by penalizing scenarios where feature diversity is compromised—specifically through assessing dissimilarity ratios between noise vectors and their corresponding synthesized features.

Together, these regularizers ensure that AFHN does not merely enhance feature variance and class separation but also fosters feature generation that respects the intra-class variability essential for FSL.

Performance Evaluation

The paper provides extensive empirical evaluations of AFHN over three benchmark datasets: Mini-ImageNet, CUB, and CIFAR100. Results highlight that the model achieves state-of-the-art performance on Mini-ImageNet and CIFAR100, showcasing significant improvements in 5-way 1-shot and 5-way 5-shot settings over existing methods, including both data augmentation and metric learning approaches.

• Mini-ImageNet: AFHN exhibits superior performance with a notable improvement over traditional and contemporary metric learning methods. The distinction is most pronounced against similar augmentation methods, including MetaGAN and Dual TriNet.
• CUB and CIFAR100: Although CUB results are more competitive, AFHN still demonstrates a lead or comparability with other top-performing methods, reaffirming its effectiveness. The gains on CIFAR100 further demonstrate its adaptability across datasets with diverse characteristics.

Implications and Future Directions

The success of AFHN underlines the importance of adversarial networks in enhancing FSL's capabilities by addressing data scarcity and feature hallucination effectively. Practically, the insights and methodology could be adapted to various domains requiring quick generalization from sparse data, including medical imaging, autonomous driving, and resource-constrained IoT applications.

Moving forward, AFHN and similar models might explore:

• Integration with Meta-Learning: Combining AFHN with meta-leaning strategies could potentially enhance the adaptation ability of FSL models even further.
• Cross-Domain Applications: Testing and refining the model's adaptability to cross-domain tasks where domain shifts are significant.
• Theoretical Underpinnings: Further investigation into the theoretical aspects of mode collapse and diversity promotion could refine regularizers and potentially introduce new ones.

In conclusion, the authors of this paper present a compelling advancement in few-shot learning by solving fundamental challenges in feature synthesis with adversarial frameworks. This contribution lays a foundation for more robust FSL systems capable of addressing real-world challenges in data-scarce environments.