281 papers with code • 16 benchmarks • 18 datasets
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces.
Ranked #8 on Domain Adaptation on Office-Caltech
To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory.
Ranked #2 on Unsupervised Domain Adaptation on Duke to MSMT
To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties.
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
We consider the problem of unsupervised domain adaptation in semantic segmentation.
Ranked #15 on Synthetic-to-Real Translation on SYNTHIA-to-Cityscapes (MIoU (13 classes) metric)
As a result, the performance of the trained semantic segmentation model on the target domain is boosted.
Ranked #6 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
Ranked #2 on Semantic Segmentation on GTAV-to-Cityscapes Labels