Adversarial feature calibration network for few-shot learning

This is the authors' abstract. We don't add key points for this paper.

Big data is the bottleneck of deep learning in the current scenario, as data itself comes with the drawbacks of expensive collection costs and even the inability to gather it at all. How to achieve good learning performance in situations with insufficient sample size has increasingly gained attention. The practical value of small sample learning is self-evident, as this technique aims to learn concepts of new classes through a few labeled samples. Data augmentation is the most intuitive approach to address small sample learning, and recent works have demonstrated its feasibility by proposing various data synthesis models. However, data augmentation during model training has a significant drawback. It can easily lead to over-fitting since it relies on a biased distribution formed by only a few training examples. In this paper, we propose a method to generate high-quality pseudo-samples by calculating a regularization factor that constrains the model generator based on statistical distribution information from a large number of classes.