GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning

arXiv:2203.07738v4 Announce Type: replace
Abstract: Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the pre-train phase employs the base data to train a CNN-based feature extractor. (ii) the meta-test phase applies the frozen feature extractor to novel data (novel data has different categories from base data) and designs a classifier for recognition. To correct few-shot data distribution, researchers propose Semi-Supervised Few-Shot Learning (SSFSL) …

abstract arxiv attention cnn cs.cv data feature few-shot few-shot learning framework graph meta popular semi-supervised test train training type