Domain generalization (DG) assumes a model is trained from multiple observed domains while it is expected to perform well on any unseen domains. PACS consists of Art painting, Cartoon, Photo and Sketch domains, which so far considers the largest domain shift as it is from the different image style depictions.
|Base Model||Methods||Venue||Art painting||Cartoon||Photo||Sketch||Ave|
|AlexNet||Li et al.||ICCV17||62.86||66.97||89.50||57.51||69.21|
|AlexNet||Li et al.||AAAI18||66.23||66.88||88.00||58.96||70.01|
|AlexNet||Massimiliano et al.||ICIP18||64.1||66.8||90.2||60.1||70.3|
|AlexNet||anonymous||Under review in ICLR19||68.1||78.6||92.1||66.1||76.2|
|AlexNet||anonymous||Under review in ICLR19||66.8||69.7||87.9||56.3||70.2|
|AlexNet||anonymous||Under review in ICLR19||61.2||63.8||82.9||59.0||66.7|
 Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales. Deeper, broader and artier domain generalization. In ICCV, 2017.
 Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales. Learning to generalize: Meta-learning for domain generalization. In AAAI, 2018.
 Massimiliano Mancini, Samuel Rota Bulo`, Barbara Caputo, Elisa Ricci. Best sources forward: domain generalization through source-specific nets. In ICIP, 2018.