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A Domain Adaptation Regularization for Denoising Autoencoders

St├ęphane Clinchant, Gabriela Csurka, Boris Chidlovskii
Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by (Ganin and Lempitsky, 2015). We build on their work by suggesting a more appropriate regularization for denoising autoencoders. Our model remains unsupervised and can be computed in a closed form. On standard text classification adaptation tasks, our approach yields the state of the art results, with an important reduction of the learning cost.
ACL, Berlin, Germany, August 7-12, 2016