58 datasets found

The dataset used in the paper for domain adaptation for sustainable soil management using causal and contrastive constraint minimization.

The dataset used for the proposed Joint Multi-Domain Learning for Automatic Short Answer Grading.

The dataset used for sim-to-real domain adaptation in robotics, consisting of tasks with randomized dynamics.

The dataset used in this paper for unsupervised domain adaptation.

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning....

Select-by-Distinctive-Margin (SDM) is a simple yet effective active learning method for active domain adaptation.

The Office-31 dataset is a benchmark dataset for domain adaptation, containing images from the Amazon domain.

The Office+Caltech-10 dataset is a benchmark dataset for domain adaptation, combining Office-10 and Caltech-10 datasets.

Generating artistic portraits is a challenging problem in computer vision. Existing portrait stylization models that generate good quality results are based on Image-to-Image...

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.

The proposed method is implemented by the Pytorch framework using a NVIDIA GeForce RTX 3090 GPU with 24GB memory.

The PointDA-10 dataset consists of three subsets of three widely-used datasets: ShapeNet, ModelNet, and ScanNet. All three subsets share the same ten distinct classes.

The Synthia dataset is a large-scale urban scene understanding dataset, containing 9000 samples. It is used for semantic segmentation tasks.

A dataset for the study of domain adaptation solutions, composed of synthetic and real videos from seven gesture classes, from both the ground and air perspectives.

Stargan v2 dataset

The Hyper-Kvasir→Piccolo task is a domain adaptation task for semantic segmentation, where the source domain is Hyper-Kvasir and the target domain is Piccolo.

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

The Office-Home dataset is a visual domain adaptation task, where the goal is to adapt a model trained in a source domain to a target domain with different distributions.

The DomainBed dataset is a benchmark for domain generalization, containing five multi-domain datasets (VLCS, PACS, OfficeHome, TerraIncognita, and DomainNet). The dataset is...

The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal...