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With the development of deep convolutional neural networks in recent years, the network structure has become more and more complicated and varied, and there are very good results in pattern recognition, image classification, scene classification, and target tracking. This end-to-end learning model relies on the initial large dataset. However, many data are gradually obtained in practical situations, which contradict the deep learning of one-time batch learning. There is an urgent need for an incremental learning approach that can continuously learn new knowledge from new data while retaining what has already been learned. This paper proposes an incremental learning algorithm based on convolutional neural network and support vector data description. CNN and AM-Softmax loss function are used to represent and continuously learn image features. Support vector data description is used to construct multiple hyperspheres for new and old classes of images. Class-incremental learning is achieved by the increment of hyperspheres. The experimental results show that the incremental learning method proposed in this paper can effectively extract the latent features of the image and adapt it to the learning situation of the class-increment. The recognition accuracy is close to batch learning.

Class-Incremental Learning Based on Feature Extraction of CNN With Optimized Softmax and One-Class Classifiers