Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation able overcome this challenge by transferring knowledge from source an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations enable successful adaptation. ...

Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by (Ganin and Lempitsky, 2015). We build on their work by suggesting a more appropriate regularizati...

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasingly more popularity. While extensive studies have been devoted improving model accuracy domain, an important issue of robustness neglected. To make things worse, conventional adversarial trainin...

Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large gap on raw data representation with disparate point densities and arrangements. By exploring domain-invariant geometric characteristics motion patterns, we present an unsupervised domain method that overcomes above difficulties. First, propose Spatial Geometry Alignment module extract similar shape features of same o...

Unsupervised domain adaptation aims at learning a classification model robust to data distribution shift between labeled source and an unlabeled target domain. Most existing approaches have overlooked the multi-dimensional nature of visual data, building models in vector space. Meanwhile, issue limited training samples is rarely considered by previous methods, yet it ubiquitous practical applic...