Yaroslav Ganin

TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.

TL;DR: In this paper, a gradient reversal layer is proposed to promote the emergence of deep features that are discriminative for the main learning task on the source domain and invariant with respect to the shift between the domains.

TL;DR: The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets.

TL;DR: A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer.

TL;DR: A simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning is proposed, leading to higher obtained CPU speedups at the cost of lower accuracy drops for the smaller of the two networks.