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Found 4 papers, 3 papers with code
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains.
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Given the use of prototypes, the number of parameters required for our PMT method does not increase significantly with the number of source domains, thus reducing memory issues and possible overfitting.
Multi-Source Unsupervised Domain Adaptation
object-detection
+2
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains
Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets.
Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation
Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w. r. t.
Ranked #3 on
Multi-target Domain Adaptation
on Office-31
