Pairing samples strategically during training can significantly reduce variance in domain adaptation, leading to better optimization and improved performance on shifted datasets.
This paper tackles high variance in domain adaptation losses by proposing PSDA, a sampling technique that pairs observations within and across domains to reduce gradient variance during training. The method reformulates the pairing problem as linear assignment and shows improved accuracy on domain shift tasks.