DomainNet

doi:doi:10.57702/8wid714l

DomainNet

The dataset used in the paper is a cross-domain dataset, consisting of six domains: Real, Painting, Sketch, Clipart, Infograph, and Quickdraw. Each domain contains 345 object classes.

BibTex:

@dataset{Xingchao_Peng_and_Qinxun_Bai_and_Xide_Xia_and_Zijun_Huang_and_Kate_Saenko_and_Bo_Wang_2024,
    abstract = {The dataset used in the paper is a cross-domain dataset, consisting of six domains: Real, Painting, Sketch, Clipart, Infograph, and Quickdraw. Each domain contains 345 object classes.},
    author = {Xingchao Peng and Qinxun Bai and Xide Xia and Zijun Huang and Kate Saenko and Bo Wang},
    doi = {10.57702/8wid714l},
    institution = {No Organization},
    keyword = {'Deep learning', 'Domain Adaptation', 'Domain Generalization', 'Domain adaptation', 'DomainNet', 'DomainNet dataset', 'Image Classification', 'Image classification', 'Multi-source domain adaptation', 'Vision Transformers', 'computer vision', 'cross-domain', 'domain adaptation', 'domain generalization', 'image classification', 'image dataset', 'large scale', 'multi-source', 'multi-source learning'},
    month = {dec},
    publisher = {TIB},
    title = {DomainNet},
    url = {https://service.tib.eu/ldmservice/dataset/domainnet},
    year = {2024}
}