Diversity Transfer Network for Few-Shot Learning (1912.13182v1)

Abstract: Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in the lack of intra-class diversity within insufficient training samples. To alleviate this problem, we propose a novel generative framework, Diversity Transfer Network (DTN), that learns to transfer latent diversities from known categories and composite them with support features to generate diverse samples for novel categories in feature space. The learning problem of the sample generation (i.e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works. Besides, an organized auxiliary task co-training over known categories is proposed to stabilize the meta-training process of DTN. We perform extensive experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet, CIFAR100 and CUB. The results show that DTN, with single-stage training and faster convergence speed, obtains the state-of-the-art results among the feature generation based few-shot learning methods. Code and supplementary material are available at: \texttt{https://github.com/Yuxin-CV/DTN}

Overview of "Diversity Transfer Network for Few-Shot Learning"

"Mengting Chen et al.'s paper, titled 'Diversity Transfer Network for Few-Shot Learning,' proposes a novel approach to tackle the challenges involved in few-shot learning tasks. Differing from traditional deep learning models that require extensive amounts of training data, few-shot learning aims to generalize from limited samples, which poses a significant hurdle due to the minimal intra-class diversity available within such small datasets.

Key Contributions and Approach

The crux of the paper is the introduction of the Diversity Transfer Network (DTN), a generative framework designed to enhance the diversity of few-shot learning samples. The DTN framework transfers latent diversities from known categories to generate novel samples for classes with few samples. This is accomplished through a feature generator that leverages the differences between pairs of samples from known categories, effectively enriching the support features in the latent space.

The feature generation process is integrated into a meta-learning framework, wherein a single-stage network minimizes a meta-classification loss to learn effectively from generated samples, diverging from the multi-stage loss optimization seen in prior works. An auxiliary task co-training mechanism called Organized Auxiliary Task co-Training (OAT) stabilizes the meta-training process and expedites convergence.

Experimental Results

The paper presents extensive experimentation across three benchmark datasets: miniImageNet, CIFAR100, and CUB. DTN achieves state-of-the-art results among feature generation-based few-shot learning methodologies, exhibiting rapid convergence and proving its effectiveness in scenarios with minimal training data. Experimental results highlight that DTN, with single-stage training and faster convergence speed, surpasses competitors in both 5-way 1-shot and 5-shot tasks.

Implications and Future Outlook

Practically, DTN offers a promising solution for applications dependent on limited data, such as medical diagnostics and personalized education technologies. Theoretically, it contributes to the discourse on enhancing intra-class diversity in few-shot learning. Future research directions may include exploring DTN's applicability in varied contexts like semi-supervised learning and reinforcement learning, extending its paradigms for accelerated learning across diverse domains.

Conclusion

In summary, 'Diversity Transfer Network for Few-Shot Learning' provides a significant methodological advancement in generative models for few-shot learning. The paper suggests that the judicious transfer of latent diversities could address the inherent scarcity of data in low-shot scenarios, offering both practical and theoretical benefits to the field of machine learning."