Energy-constrained Self-training for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2101.00316v1
• Date: Fri, 1 Jan 2021 21:02:18 GMT
• Title: Energy-constrained Self-training for Unsupervised Domain Adaptation
• Authors: Xiaofeng Liu, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Lingsheng Kong
• Abstract summary: Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-training involves an iterative process of predicting on the target domain and then taking the confident predictions as hard pseudo-labels for retraining. In this paper, we resort to the energy-based model and constrain the training of the unlabeled target sample with the energy function minimization objective.
• Score: 25.594991545790638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-training involves an iterative process of predicting on the target domain and then taking the confident predictions as hard pseudo-labels for retraining. However, the pseudo-labels are usually unreliable, and easily leading to deviated solutions with propagated errors. In this paper, we resort to the energy-based model and constrain the training of the unlabeled target sample with the energy function minimization objective. It can be applied as a simple additional regularization. In this framework, it is possible to gain the benefits of the energy-based model, while retaining strong discriminative performance following a plug-and-play fashion. We deliver extensive experiments on the most popular and large scale UDA benchmarks of image classification as well as semantic segmentation to demonstrate its generality and effectiveness.

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