Search
Search Results
-
Domain consensual contrastive learning for few-shot universal domain adaptation
Traditional unsupervised domain adaptation (UDA) aims to transfer the learned knowledge from a fully labeled source domain to another unlabeled...
-
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation
Universal domain adaptation aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge...
-
Towards adaptive unknown authentication for universal domain adaptation by classifier paradox
Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation....
-
Universal unsupervised cross-domain 3D shape retrieval
Most existing cross-domain 3D shape retrieval (CD3DSR) methods have assumed the setting of a fixed kind of query set (source domain), and all the...
-
Introduction to Domain Adaptation
Domain adaptation refers to the machine learning techniques that enable models trained on data from a source domain to perform well on a different...
-
Universal Model Adaptation by Style Augmented Open-set Consistency
Learning to recognize unknown target samples is of great importance for unsupervised domain adaptation (UDA). Open-set domain adaptation (OSDA) and...
-
TANet: Adversarial Network via Tokens Transformer for Universal Domain Adaptation
Universal Domain Adaptation (UDA) aims to transfer knowledge between two datasets. The main challenge is to distinguish “unknown” classes that do not...
-
Unsupervised Domain Adaptation Techniques
This chapter provides an overview of unsupervised domain adaptation techniques. First, we identify key challenges and limitations in current...
-
Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization
In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study...
-
Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision
Although neural approaches have yielded state-of-the-art results in the sentence matching task, their performance inevitably drops dramatically when...
-
Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation
Source-free domain adaptation (SFDA) aims to train a well-performed model in the target domain given both a trained source model and unlabeled target...
-
Universal Representations: A Unified Look at Multiple Task and Domain Learning
We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations , a single deep neural...
-
Rethinking confidence scores for source-free unsupervised domain adaptation
Source-free unsupervised domain adaptation (SFUDA) aims to achieve target domain predictions through a source model instead of source data. One of...
-
CLIP-guided black-box domain adaptation of image classification
Recently, the significant success of the large pre-trained models have attracted great attentions. How to sufficiently use these models is a big...
-
Open-set domain adaptation by deconfounding domain gaps
Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any...
-
Dual collaboration for decentralized multi-source domain adaptation
The goal of decentralized multi-source domain adaptation is to conduct unsupervised multi-source domain adaptation in a data decentralization...
-
Continual Test-Time Unsupervised Domain Adaptation
Continual test-time domain adaptation (TTA) is a challenging topic in the field of source-free domain adaptation, which focuses on addressing...
-
mixDA: mixup domain adaptation for glaucoma detection on fundus images
Deep neural network has achieved promising results for automatic glaucoma detection on fundus images. Nevertheless, the intrinsic discrepancy across...
-
Active Learning for Unsupervised Domain Adaptation
Active learning methods have been explored to improve UDA by actively annotating a small subset of informative target domain samples. This chapter...
-
Handling Domain Shift for Lesion Detection via Semi-supervised Domain Adaptation
As the community progresses towards automated Universal Lesion Detection (ULD), it is vital that the techniques developed are robust and easily...