Unsupervised domain adaptive object detection aims to adapt detectors from a labelled source an unlabelled target domain. Most existing works take two-stage strategy that first generates region proposals and then detects objects of interest, where adversarial learning is widely adopted mitigate the inter-domain discrepancy in both stages. However, may impair alignment well-aligned samples as it...

Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained one domain to another target domain, usually performs poorly. To tackle this problem, unsupervised adaptation (UDA) techniques are proposed bridge gap domains, for purpose improving performance without annotation domain. Particularly, UDA h...

As a category of transfer learning, domain adaptation plays an important role in generalizing the model trained one task and applying it to other similar tasks or settings. In speech enhancement, well-trained acoustic can be exploited obtain signal context languages, speakers, environments. Recent research was developed more effectively with various neural networks high-level abstract features....

Unsupervised domain adaptation aims to align the distributions of data in source and target domains, as well assign labels domain. In this paper, we propose a new method named Domain Adaptation based on Pseudo-Label Confidence (UDA-PLC). Concretely, UDA-PLC first learns feature representation by projecting domains into latent subspace. subspace, distribution two are aligned discriminability fea...