Improving classification with semi-supervised and fine-grained learning

Abstract In this paper, we propose a novel and efficient multi-stage approach, which combines both semi-supervised learning and fine-grained learning to improve the performance of classification model learned only from a few samples. The fine-grained category recognition process utilized in our method is dubbed as MSR. In this process, we cut images into multi-scaled parts to feed into the network to learn more fine-grained features. By assigning these image cuts with dynamic weights, we can reduce the negative impact of background information and thus achieve a more accurate prediction. Furthermore, we present the voted pseudo label (VPL) which is an efficient method of semi-supervised learning. In this approach, for unlabeled data, VPL picks up the classes with non-confused labels verified by the consensus prediction of different classification models. These two methods can be applied to most neural network models and training methods. Inspired from classifier-based adaptation, we also propose a mix deep CNN architecture (MixDCNN). Both the VPL and MSR are integrated with the MixDCNN. Comprehensive experiments demonstrate the effectiveness of VPL and MSR. Without bottles and jars, we achieve the state-of-the-art or even better performance in two fine-grained recognition tasks on the datasets of Stanford Dogs and CUB Birds, with the accuracy of 95.6% and 85.2%, respectively.