Cross-Domain Image Processing with Machine Learning: Methods and Challenges

Abstract

Cross-domain image processing is a core problem in machine learning, where the models trained on one domain find it hard togeneralize to visually different target domains because of domain shifts. In this paper, we present a unified architecture,comprising deep feature extraction, adversarial domain adaptation, and statistical alignment by means of MMD (Maximum Mean Discrepancy), to address this challenge