Publications
Authors:
  • St├ęphane Clinchant , Gabriela Csurka , Boris Chidlovskii
Citation:
ACL, Berlin, Germany, August 7-12, 2016
Abstract:
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.
Year:
2016
Report number:
2016/019