Unsupervised Domain Adaptation via Bidirectional Transmission Generator Self-Training

Unsupervised domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the fully unlabeled target domain, thus improving the classification performance of the target domain. Recently, self-training methods have shown their effectiveness on UDA. It iteratively trains target data using the generated target pseudo-labels. However, the feature space for generating pseudo-labels contains a large amount of source information, which traps the model in the source domain, making it challenging for the generator to learn discriminative features of the target domain. In this article, we propose a self-training domain adaptation (DA) model with bidirectional transmission generators (BDTGs). Specifically, we design a bidirectional transmission structure for generators, using exponential moving average (EMA) as the bridge between two generators. The structure has two advantages: 1) by transmitting weight parameters to each other during the training process, it promotes the shift of the feature space, thereby alleviating the difficulty of the model in adapting target domain features and 2) the transmission disturbs the classification boundary and is able to expose unreliable target samples near the boundary. We design a cosine similarity-based filter to identify such samples, to reduce the influence of noisy pseudo-labels with incorrect semantic information on the model. Extensive experiments conducted on five benchmark UDA datasets show that our approach has superior classification performance.