Domain-Adversarial Training of Neural Networks
Yaroslav Ganin Affiliation: Evgeniya Ustinova Affiliation: Skolkovo Institute of Science and Technology (Skoltech) Affiliation: Skolkovo, Moscow Region, Russia Hana Ajakan Affiliation: Pascal Germain Affiliation: Département d’informatique et de génie logiciel, Université Laval Affiliation: Québec, Canada, G1V 0A6 Hugo Larochelle Affiliation: Département d’informatique, Université de Sherbrooke Affiliation: Québec, Canada, J1K 2R1 François Laviolette Affiliation: Mario Marchand Affiliation: Département d’informatique et de génie logiciel, Université Laval Affiliation: Québec, Canada, G1V 0A6 Victor Lempitsky Affiliation: Skolkovo Institute of Science and Technology (Skoltech) Affiliation: Skolkovo, Moscow Region, Russia
Abstract
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
原文 arXiv:1505.07818;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1505.07818v4