MTNet: Mutual tri-training network for unsupervised domain adaptation on person re-identification

Abstract

The existing unsupervised domain adaptation (UDA) methods on person re-identification (re-ID) often employ clustering to assign pseudo labels for unlabeled target domain samples. However, it is difficult to give accurate pseudo labels to unlabeled samples in the clustering process. To solve this problem, we propose a novel mutual tri-training network, termed MTNet, for UDA person re-ID. The MTNet method can avoid noisy labels and enhance the complementarity of multiple branches by collaboratively training the three different branch networks. Specifically, the high-confidence pseudo labels are used to update each network branch according to the joint decisions of the other two branches. Moreover, inspired by self-paced learning, we employ a sample filtering scheme to feed unlabeled samples into the network from easy to hard, so as to avoid trapping in the local optimal solution. Extensive experiments show that the proposed method can achieve competitive performance compared with the state-of-the-art person re-ID methods.