Pretrained back propagation based adaptive resonance theory network for adaptive learning

The deep convolutional neural network performs well in current computer vision tasks. However, most of these models are trained on an aforehand complete dataset. New application scenario data sets should be added to the original training data set for model retraining when application scenarios change significantly. When the scenario changes only slightly, the transfer learning can be used for network training by a small data set of new scenarios to adapt it to the new scenario. In actual application, we hope that our model has bio-like intelligence and can adaptively learn new knowledge. This paper proposes a pretrained adaptive resonance network (PAN) based on the CNN and an intra-node back propagation ART network, which can adaptively learn new knowledge using prior information. The PAN network explores the difference between the new data and the stored information and learns this difference to realize the adaptive growth of the network. The model is testified on the MNIST and Omniglot data set, which show the effectiveness of PAN in adaptive incremental learning and its competitive classification accuracy.