Title

Domain Adaptation with Unsupervised Adversarial Learning

Abstract

Learning a discriminative classifier or other predictor in the presence of a shift between training and test distributions is known as domain adaptation (DA)” (Ganin et al., 2015).
Introduced by Ganin et al. (2015, 2016), Adversarial Domain Adaptation is a prominent method for addressing this distributional shift. In this approach, an additional neural network, known as a domain discriminator, is trained to classify the original domains of the inputs. By utilizing adversarial learning, the feature extractor is adapted to confuse the discriminator, thereby reducing the discrepancy between the source and target distributions in the latent (feature) space while preserving relevant information for the primary task. This framework effectively promotes domain-invariant representations and improves model generalization across different data domains.