Auxiliary Task-Guided CycleGAN for Black-Box Model Domain Adaptation

Michael Essich, Markus Rehmann, Cristóbal Curio; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 541-550

The research area of domain adaptation investigates methods that enable the transfer of existing models across different domains, e.g., addressing environmental changes or the transfer from synthetic to real data. Especially unsupervised domain adaptation is beneficial because it does not require any labeled target domain data. Usually, existing methods are targeted at specific tasks and require access or even modifications to the source model and its parameters which is a major drawback when only a black-box model is available. Therefore, we propose a CycleGAN-based approach suitable for black-box source models to translate target domain data into the source domain on which the source model can operate. Inspired by multi-task learning, we extend CycleGAN with an additional auxiliary task that can be arbitrarily chosen to support the transfer of task-related information across domains without the need for having access to a differentiable source model or its parameters. In this work, we focus on the regression task of 2D human pose estimation and compare our results in four different domain adaptation settings to CycleGAN and RegDA, a state-of-the-art method for unsupervised domain adaptation for keypoint detection.