We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces – a fully labeled source stream and an unlabeled target are learned together. Unique characteristics challenges such as covariate shift, asynchronous concept drifts, contrasting throughput arise. propose ACDC, adversarial domain adaptation framewor...

Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source to an unlabeled target where two domains have distinctive data distributions. Thus, essence is mitigate distribution divergence between domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing metric which defines gaps. In paper, ...

We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predictions. One important instance of this problem area occurs in settings where it is more costly to make a new error than to repeat a previous error. We propose several methods for learning effectively in this setting, and test them empirically on the real-w...

We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source” data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms stateof-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multidomain adap...

Great progress has been achieved in domain adaptation decades. Existing works are always based on an ideal assumption that testing target domains independent and identically distributed with training domains. However, due to unpredictable corruptions (e.g., noise blur) real data, such as web images real-world object detection, methods increasingly required be corruption robust We investigate a ...

Domain Adaptation aiming to learn a transferable feature between different but related domains has been well investigated and shown excellent empirical performances. Previous works mainly focused on matching the marginal distributions using adversarial training methods while assuming conditional relations source target domain remained unchanged, i.e., ignoring shift problem. However, recent hav...