# A Broad Study of Pre-training for Domain Generalization and Adaptation Source: [A Broad Study of Pre-training for Domain Generalization and Adaptation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:25:36.384Z Authors: Donghyun Kim, Kaihong Wang, Stan Sclaroff, Kate Saenko Published: 2022-03-22T15:38:36.000Z ## Abstract Deep models must learn robust and transferable representations in order to perform well on new domains. While domain transfer methods (e.g., domain adaptation, domain generalization) have been proposed to learn transferable representations across domains, they are typically applied to ResNet backbones pre-trained on ImageNet. Thus, existing works pay little attention to the effects of pre-training on domain transfer tasks. In this paper, we provide a broad study and in-depth analysis of pre-training for domain adaptation and generalization, namely: network architectures, size, pre-training loss, and datasets. We observe that simply using a state-of-the-art backbone outperforms existing state-of-the-art domain adaptation baselines and set new baselines on Office-Home and DomainNet improving by 10.7\\% and 5.5\\%. We hope that this work can provide more insights for future domain transfer research. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()