Jie Geng

Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning

Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This w…