• Matlab Code for "Unsupervised Domain Adaptation with Regularized Domain Instance Denoising", Csurka, Gabriela and Chidlovskii, Boris and Clinchant, Stéphane and Michel, Sofia in ECCV workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV), 2016 Paper Here

• Python Code for ACL'16: Link to Paper: A Domain Adaptation Regularization for Denoising Autoencoders" Contains also implemenation of marginalized stacked denoising autoencoders Chen et al, ICML'12.

#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 (ICML'15). We build on their work by suggesting a more appropriate regularization for denoising autoencoders and propose to extend the marginalized denoising autoencoder (MDA) framework with a domain regularization whose aim is to denoise both the source and target data in such a way that the features become domain invariant and the adaptation gets easier. The domain regularization, based either on the maximum mean discrepancy (MMD) measure or on the domain prediction, aims to reduce the distance between the source and the target data. We also exploit the source class labels as another way to regularize the loss, by using a domain classifier regularizer. Our model remains unsupervised and can be computed in a closed form.

#Licence Cf the LICENCE File; Copyright 2016 Xerox Corporation (“Xerox”)

#Python Dependencies

• sklearn
• numpy
• scipy