35 code implementations • 28 May 2015 • Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky
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.
Ranked #2 on Domain Adaptation on Synth Digits-to-SVHN
1 code implementation • 15 Dec 2014 • Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand
We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification task, but uninformative as to the domain of the input.