Unsupervised descriptor selection based meta-learning networks for few-shot classification

Meta-learning aims to train a classifier on collections of tasks, such that it can recognize new classes given few samples from each. However, current approaches encounter overfitting and poor generalization since the internal representation learning is obstructed by backgrounds and noises in limited samples. To alleviate those issues, we propose the Unsupervised Descriptor Selection (UDS) to tackle few-shot learning tasks. Specifically, a descriptor selection module is proposed to localize and select semantic meaningful regions in feature maps without supervision. The selected features are then mapped into novel vectors by a task-related aggregation module to enhance internal representations. With a simple network structure, UDS makes adaptation between tasks more efficient, and improves the performance in few-shot learning. Extensive experiments with various backbones are conducted on Caltech-UCSD Bird and miniImageNet, indicate that UDS achieves the comparable performance to state-of-the-art methods, and improves the performance of prior meta-learning methods.