Dual Prototypical Network for Robust Few-shot Image Classification

Deep neural networks have outperformed humans on some image recognition and classification tasks. However, with the emergence of various novel classes, it remains a challenge to continuously expand the learning capability of such networks from a limited number of labeled samples. Metric-based approaches have been playing a key role in few-shot image classification, but most of them measure the distance between samples in the metric space using only a single metric function. In this paper, we propose a Dual Prototypical Network (DPN) to improve the test-time robustness of the classical prototypical network. The proposed method not only focuses on the distance of the original features, but also adds perturbation noise to the image and calculates the distance of noisy features. By enforcing the model to predict well under both metrics, more representative and robust class prototypes are learned and thus lead to better generalization performance. We validate our method on three fine-grained datasets in both clean and noisy settings.