Fast Few-Shot Learning Algorithm Based on Deep Network
DAI Leichao1, FENG Lin1, SHANG Xinglin1, SU Han1, GONG Xun2
1. College of Computer Science, Sichuan Normal University, Chengdu 610101 2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756
Abstract：The cognitive process of the few-shot learning method simulating human learning from a small number of samples is one of the hotspots in the machine learning field. To solve the problems of large task volume and serious overfitting in the iterative process of the current few-shot learning methods, a fast few-shot learning algorithm based on deep network is proposed. Firstly, the kernel density estimation and image filtering methods are utilized to add multiple types of random noise to the training set to generate support sets and query sets. Then, the prototype network is applied to extract the image features of the support set and query set. According to the Bregman divergence, the center point of the support sample of each type of support set is employed as the class prototype. Then, the L2 norm is utilized to measure the distance between the support set and the query image. Multiple heterogeneous base classifiers are generated using cross-entropy feedback loss. Finally, the voting mechanism is introduced to fuse the nonlinear classification results of the base classifiers. Experiments show that the proposed algorithm speeds up the convergence of few-shot learning with higher classification accuracy and strong robustness.
代磊超, 冯林, 尚兴林, 苏菡, 龚勋. 基于深度网络的快速少样本学习算法[J]. 模式识别与人工智能, 2021, 34(10): 941-956.
DAI Leichao, FENG Lin, SHANG Xinglin, SU Han, GONG Xun. Fast Few-Shot Learning Algorithm Based on Deep Network. , 2021, 34(10): 941-956.
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