Synthetic Dataset Acquisition for a Specific Target Domain

Abstract

Intelligent sampling from simulation becomes crucial due to storage and hardware constraints. This research focuses on developing an intelligent acquisition strategy for synthetic data and evaluates multiple approaches to address the limitations of existing domain adaptation methods. Selecting suitable synthetic data for real-world model training presents challenges, as accurately representing the real world remains elusive. We tackle the task of adapting from synthetic to real-world data through unsupervised domain adaptation, a challenging setting for perception systems. The performance of our acquisition function is measured by its facilitation of this adaptation. We showcase different strategies, to assign value to synthetic images. Acquisition functions either operate based on synthetic data alone or take the given real world target domain into account, to assign a value to synthetic images. Leveraging assumptions from semi-supervised learning, we identify challenging real-world images and find their counterparts in the synthetic world. Evaluation is conducted using the GTA-5 dataset as the representative synthetic world and the Cityscapes and ACDC dataset as the target domain. State-of-the-art unsupervised domain adaptation approaches are employed to assess the effectiveness of our acquisition function. By advancing the utilization of synthetic data in training perception systems, this research contributes to improved real-world performance. Our findings demonstrate the potential of intelligent acquisition strategies for enhancing the adaptation from synthetic to real-world domains.