A Domain Adaptation Regularization for Denoising Autoencoders
Stéphane 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