SPL: Exploiting Unlabeled Data for Multi-label Image Classification

Abstract

The utilization of the unlabeled data provides a beneficial attempt for improving the generalization ability of the convolutional neural network (CNN) model, just as what is applied in person re-identification task. Different from that, multi-label image classification aims to predict multiple labels for each given image. The unlabeled data should be properly assigned multiple labels for regularizing the training process of CNN model. To make full use of the unlabeled data, this paper proposes a soft pseudo labeling (SPL) method for multi-label image classification. Specifically, the unlabeled samples are first generated by DCGAN and WGAN-GP. Then, the virtual multiple labels of the generated unlabeled samples are assigned based on an initial confidence value by SoftMax function. Finally, both the generated samples and original training samples are fed into the network as input, in order to learn a CNN model with stronger generalization ability. On three public multi-label image classification datasets (i.e., WIDER-Attribute, NUS-WIDE and MS-COCO), SPL provides a stable improvement over the baseline and produces a competitive performance compared with some existing multi-label image classification methods.