Classification of Long-Tailed Data Based on Bilateral-Branch Generative Network with Time-Supervised Strategy

Abstract

In the face of the long-tailed data distribution that widely exists in real-world datasets, this paper proposes a bilateral-branch generative network model. The data of the second branch is constructed by resampling the generative network training method to improve the data quality. A bilateral-branch network model is used to curb the risk of gradient explosion and to avoid over-fitting and under-fitting with the combined effect of different data branches. Meanwhile, Time-supervised strategy is introduced to improve the model's operational efficiency and ability to cope with extreme conditions by supervising and collaboratively controlling of the bilateral-branch generative network with time-invariant parameters. Time supervised strategy could ensure the accuracy of the model while reducing the number of iterations. Experimental results on two publicly available datasets, CIFAR10 and CIFAR100, show that the proposed method effectively improves the performance of long-tail data classification.