关键词: 小样本分类, 强化学习, 自动裁剪, 关系网络, 特征映射

Abstract: For the difficulty of achieving high-precision classification in deep learning algorithms for small training samples, a small-sample classification algorithm based on reinforcement learning and relational network is proposed. An automatic cropping model based on reinforcement learning aesthetic aware is adopted in the process of image preprocessing, and an optimal cropped image is output by constructing an aesthetic aware reward function so as to retain the most characteristic part of the image. The relation network architecture is utilized, the training samples and test samples in the auto-cropped small-sample image are subjected to extract feature through an embedded module in the relation network. The extracted features are cascaded to the feature map, and the cascaded feature maps are fed into the relational module in the relational network for comparison, and the resulting relation scores in the range of 0 to 1 are used as comparison results to determine the category to which the test samples belong. Experiments on small-sample datasets and comparison with the existing methods, experiments show that this method can achieve high precision of small-sample classification.

Key words: small-sample classification, reinforcement learning, automatic cropping, relational network, feature maps