A convex Kullback–Leibler optimization for semi-supervised few-shot learning

Few-shot learning has achieved great success in many fields, thanks to its requirement of limited number of labeled data. However, most of the state-of-the-art techniques of few-shot learning employ transfer learning, which still requires massive labeled data to train a meta-learning system. To simulate the human learning mechanism, a deep model of few-shot learning is proposed to learn from one, or a few examples. First of all in this paper, we analyze and note that the problem with representative semi-supervised few-shot learning methods is getting stuck in local optimization and the negligence of intra-class compactness problem. To address these issue, we propose a novel semi-supervised few-shot learning method with Convex Kullback–Leibler, hereafter referred to as CKL, in which KL divergence is employed to achieve global optimum solution by optimizing a strictly convex functions to perform clustering; whereas sample selection strategy is employed to achieve intra-class compactness. In training, the CKL is optimized iteratively via deep learning and expectation–maximization algorithm. Intensive experiments have been conducted on three popular benchmark data sets, take miniImagenet data set for example, our proposed CKL achieved 76.83% and 85.78% under 5-way 1-shot and 5-way 5-shot, the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and obtains the start-of-the-art performance.