Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled similar yet shifted target domain. Though achieving good performance, these are inapplicable for Multivariate Time-Series (MTS) data. MTS data collected from multiple sensors, each of which follows various distributions. However,...

Unsupervised domain adaption (UDA) is a promising solution to enhance the generalization ability of model from source target without manually annotating labels for data. Recent works in cross-domain object detection mostly resort adversarial feature adaptation match marginal distributions two domains. However, perfect alignment hard achieve and likely cause negative transfer due high complexity...

The segmentation of unlabeled medical images is troublesome due to the high cost annotation, and unsupervised domain adaptation one solution this. In this paper, an improved method was proposed. proposed considered both global alignment category-wise alignment. First, we aligned appearance two domains by image transformation. Second, output maps in a way. Then, decomposed semantic prediction ma...

Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based CT denoising algorithms were primarily based on supervised learning. However, learning requires a large number of training samples, which is impractical real-world scenarios. To address this problem, we propose novel unsupervised domain adaptation approach for This propos...

In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment unlabeled real data using labeled synthetic data. The main problem of UDA segmentation relies on reducing gap between image and image. To solve problem, focused separating information in an into content style. Here, only has cues style makes gap. Thus, precise separation leads effe...