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Deep Neural Networks (DNNs) have recently been achieving state-of-the-artperformance on a variety of computer vision related tasks. However, theircomputational cost limits their ability to be implemented in embedded systemswith restricted resources or strict latency constraints. Model compression hastherefore been an active field of research to overcome this issue.Additionally, DNNs typically require massive amounts of labeled data to betrained. This represents a second limitation to their deployment. DomainAdaptation (DA) addresses this issue by allowing knowledge learned on onelabeled source distribution to be transferred to a target distribution,possibly unlabeled. In this paper, we investigate on possible improvements ofcompression methods in DA setting. We focus on a compression method that waspreviously developed in the context of a single data distribution and showthat, with a careful choice of data to use during compression and additionalregularization terms directly related to DA objectives, it is possible toimprove compression results. We also show that our method outperforms anexisting compression method studied in the DA setting by a large margin forhigh compression rates. Although our work is based on one specific compressionmethod, we also outline some general guidelines for improving compression in DAsetting.

Domain Adaptation Regularization for Spectral Pruning