In this paper we address the problem of domain adaptation using multiple source domains. We extend the XRCE contribution to Clef’14 Domain Adaptation challenge  with the new methods and new datasets. We describe a new class of domain adaptation technique based on stacked marginalized denoising autoencoders (sMDA). It aims at extracting and denoising features common to both source and target domains in the unsupervised mode. Noise marginalization allows to obtain a closed form solution and to considerably reduce the training time. We build a classification system which compares sMDA combined with SVM or with Domain Specific Class Mean classifiers to the state-of-the art in both unsupervised and semi-supervised settings. We report the evaluation results for a number of image and text datasets.