31 datasets found

The dataset used for unsupervised cell nuclei segmentation in this paper.

Unsupervised image-to-image translation networks.

GeneIC uses CUB-200 and Oxford-102 datasets for training and testing.

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.

The dataset is a complex multi-source acoustic environment and an improved algorithm for the estimation of source-dominated microphone clusters in acoustic sensor networks.

The dataset used in the paper is a synthetic dataset for unsupervised representation learning.

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...

The dataset used in this paper is a reinforcement learning dataset, specifically the SimpleQuestion dataset, which contains questions answerable using Wikidata as the knowledge...

The MVTec Anomaly Detection (AD) dataset is a comprehensive real-world dataset for unsupervised anomaly detection.