“synopsis and explainer” methodological discussion has been reflexed, in literary domain, as stipulation and constraint; but synopsis explanation seems being different to what happens in methodology because sometimes the meanings of lexical items and combinations are given on individual, probabilistic, and untrustworthy interpretations. present paper, applying few lines of nasser khosrow’s seco...

In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised adaptation. We propose adaptation by and exploiting only meaningful from data for training. Our key assumption is that the entire may contain samples are unhelpful Therefore, can benefit composed solely helpful relevant samples. The proposed method effectively subsamples full to generate sma...

We deal with a wide range of colors in our daily life. They are such ubiquitous phenomena that is hard and next to impossible to imagine even a single entity (be it an object, place, living creature, etc) devoid of them. They are like death and tax which nobody can dispense with. This omnipresence of colors around us has also made its way through abstract and less tangible entities via the inte...

Deep neural networks (DNNs) have greatly contributed to the performance gains in semantic segmentation. Nevertheless, training DNNs generally requires large amounts of pixel-level labeled data, which is expensive and time-consuming collect practice. To mitigate annotation burden, this paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for In SE...

Measuring and alleviating the discrepancies between synthetic (source) real scene (target) data is core issue for domain adaptive semantic segmentation. Though recent works have introduced depth information in source to reinforce geometric knowledge transfer, they cannot extract intrinsic 3D of objects, including positions shapes, merely based on 2D estimated depth. In this work, we propose a n...