[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives

X Liu, C Yoo, F Xing, H Oh, G El Fakhri… - … on Signal and …, 2022 - nowpublishers.com
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Self-supervised learning for videos: A survey

MC Schiappa, YS Rawat, M Shah - ACM Computing Surveys, 2023 - dl.acm.org
The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Balancing discriminability and transferability for source-free domain adaptation

JN Kundu, AR Kulkarni, S Bhambri… - International …, 2022 - proceedings.mlr.press
Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

A review of single-source deep unsupervised visual domain adaptation

S Zhao, X Yue, S Zhang, B Li, H Zhao… - … on Neural Networks …, 2020 - ieeexplore.ieee.org
Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation

X Yue, Z Zheng, S Zhang, Y Gao… - Proceedings of the …, 2021 - openaccess.thecvf.com
Abstract Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-
labeled source domain to an unlabeled target domain. In some applications, however, it is …

Clda: Contrastive learning for semi-supervised domain adaptation

A Singh - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
Abstract Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant predictive …

A broad study of pre-training for domain generalization and adaptation

D Kim, K Wang, S Sclaroff, K Saenko - European Conference on Computer …, 2022 - Springer
Deep models must learn robust and transferable representations in order to perform well on
new domains. While domain transfer methods (eg, domain adaptation, domain …

Learning invariant representations and risks for semi-supervised domain adaptation

B Li, Y Wang, S Zhang, D Li… - Proceedings of the …, 2021 - openaccess.thecvf.com
The success of supervised learning crucially hinges on the assumption that training data
matches test data, which rarely holds in practice due to potential distribution shift. In light of …

Domain adaptation: challenges, methods, datasets, and applications

P Singhal, R Walambe, S Ramanna, K Kotecha - IEEE access, 2023 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Dual-head contrastive domain adaptation for video action recognition

VGT Da Costa, G Zara, P Rota… - Proceedings of the …, 2022 - openaccess.thecvf.com
Unsupervised domain adaptation (UDA) methods have become very popular in computer
vision. However, while several techniques have been proposed for images, much less …