Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed achieve transferrable models. Among them, most prevalent method is adversarial adaptation, which can shorten distance between source and target domain. Although learning very effective, it still leads instability of network drawbacks confusing category information. In this paper,...

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source with shift. Most of the existing UDA methods try mitigate adverse impact induced shift via reducing discrepancy. However, such approaches easily suffer a notorious mode collapse issue due lack labels in domain. Naturally, one effective ways this is reliably estimate pseudo...

Mixed cultures of Cellulomonas sp. and Azospirillum brasilense were grown with straw or cellulose as the carbon source under conditions favoring the fixation of atmospheric nitrogen. Rapid increases in cell numbers, up to 10 cells per g of substrate, were evident after 4 and 5 days of incubation at 30 degrees C for cellulose and straw, respectively. Nitrogen fixation (detected by acetylene redu...

Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging distribution gaps. For UDA-based object detection methods, majority them alleviate bias inducing domain-invariant feature generation via adversarial strategy. However, their...