Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsupervised domain adaptation algorithms have been proposed to directly address t...

Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances deep learning enable accurate semantic reconstruction of surroundings from LiDAR data. However, these models encounter large domain gap while deploying them on equipped with different setups which drastically decreases their performance. Fine-tuning model every new setup infeasib...

Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains assist learning tasks. A critical aspect of unsupervised is the more transferable and distinct feature representations different domains. Although previous investigations, using, for example, CNN-based auto-encoder-based methods, have produced remarkable results in adaptation, there are ...

Unsupervised Domain Adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from labeled source with related but different distribution. The strategy of aligning the two domains in latent feature space via metric discrepancy or adversarial has achieved considerable progress. However, these existing approaches mainly focus on adapting entire image ...

A pre-trained language model, BERT, has brought significant performance improvements across a range of natural processing tasks. Since the model is trained on large corpus diverse topics, it shows robust for domain shift problems in which data distributions at training (source data) and testing (target differ while sharing similarities. Despite its great compared to previous models, still suffe...

Recent years have witnessed great progress in synthetic aperture radar (SAR) target detection methods based on deep learning. However, these generally assume the training data and test obey same distribution, which does not always hold when parameters, imaging algorithm, viewpoints, scenes, etc., change practice. When such a distribution mismatch occurs, it will cause significant performance dr...