8 datasets found

The dataset used for extensive cut-and-paste augmentation for unsupervised domain adaptive semantic segmentation.

The dataset used in this paper for unsupervised domain adaptation.

The Synthia→Cityscapes task is a domain adaptation task for semantic segmentation, where the source domain is Synthia and the target domain is Cityscapes.

Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the...

VisDA-C is a dataset for visual domain adaptation, consisting of synthetic rendering of 3D models in the source domain and Microsoft COCO real images in the target domain.

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain.

Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...