Devices, systems, methods, and media for domain adaptation using hybrid learning

Abstract

Devices, systems, methods, and media are disclosed for domain adaptation of a trained machine learning model using hybrid learning. A hybrid approach to domain adaptation is disclosed that combines aspects of discrepancy-based, adversarial, and reconstruction-based approaches to achieve an end-to-end trained model for performing a prediction task (such as semantic segmentation) on a sparsely labeled dataset in a target domain, by leveraging a richly-labeled dataset in the source domain. Some embodiments may also provide a trained domain translation model for generating synthetic data samples in a first domain based on input data samples from a second domain.