TMDA: TaskSpecific Multisource Domain Adaptation via Clustering Embedded Adversarial Training Beyond classical domainspecific adversarial training, a recently proposed taskspecific framework has achieved a great success in single source domain adaptation by utilizing taskspecific decision boundaries. However, compared to singlesourcesingletarget setting, multisource domain adaptation (MDA) shows more powerful capability to handle with most reallife cases. To align target domain with diverse multisource domains using taskspecific decision boundaries, we provide a deep insight of taskspecific framework on MDA for the first time. Accordingly, we propose a novel taskspecific multisource domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a maxmin optimization over two adversarial taskspecific classifiers. Moreover, our analysis implies that scattered multisource representations disturb the adversarial training under the taskspecific framework. To tight up the dispersed source representations, we embeds a relationshipbased domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms stateoftheart methods on toy dataset, sentiment analysis and digit classification.

Transfer Learning with Dynamic Adversarial Adaptation Network The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global source and target distributions, or pay attention to align subdomains based on multiple discriminators. However, in real applications, the marginal (global) and conditional (local) distributions between domains are often contributing differently to the adaptation. There is currently no method to dynamically and quantitatively evaluate the relative importance of these two distributions for adversarial learning. In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domaininvariant representations while quantitatively evaluate the relative importance of global and local domain distributions. To the best of our knowledge, DAAN is the first attempt to perform dynamic adversarial distribution adaptation for deep adversarial learning. DAAN is extremely easy to implement and train in real applications. We theoretically analyze the effectiveness of DAAN, and it can also be explained in an attention strategy. Extensive experiments demonstrate that DAAN achieves better classification accuracy compared to stateoftheart deep and adversarial methods. Results also imply the necessity and effectiveness of the dynamic distribution adaptation in adversarial transfer learning.