42 datasets found

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...

PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion

The NIST-SRE 2016 dataset contains recordings of speakers from different domains.

Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability...

The ImageCLEF-DA dataset is a benchmark dataset for ImageCLEF 2014 domain adaptation challenges, which contains 12 categories shared by three domains: Caltech-256 (C), ImageNet...

The FMoW dataset aims to classify utility types from satellite images.

Real-world applications of machine learning models often confront data distribution shifts, where discrepancies exist between the training and test data distributions.

A dataset for domain adaptation for LiDAR point cloud semantic segmentation.

A boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (Li-DARNet).

Unsupervised domain adaptation framework for fundus image segmentation

The paper proposes a Towards Fair Knowledge Transfer (TFKT) framework to handle the fairness challenge in imbalanced cross-domain learning.

The dataset used in the paper is PubMed, ArXiv, and Movies. PubMed is a medical dataset consisting of research articles from the PubMed repository. The articles' subheadings...

ImageNet-Sketch is used as target dataset for domain adaptation.

SVHN, MNIST, and MNIST-M are used as source datasets for domain adaptation.

Transparent adaptation in deep medical image diagnosis.

Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity.

COVID-19 CT Database (COV19-CT-DB) is a dataset used for Covid-19 Detection and Covid-19 Domain Adaptation Challenges.

The SYNTHIA dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

The GTA5 dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

VisDA-2017 is a simulation-to-real dataset with two extremely distinct domains: Synthetic renderings of 3D models and Real collected from photo-realistic or real-image datasets.