In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptation ability. However, these DA methods typically only provide good performance after a long period of adaptation, and perform poorly on new domains before and during adaptation - in what we call the "Unfamiliar Period", especially when domain shifts happen suddenly and significantly. On the other hand, domain generalization (DG) methods have been proposed to improve the model generalization ability on unadapted domains. However, existing DG works are ineffective for continually changing domains due to severe catastrophic forgetting of learned knowledge. To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains. RaTP includes a training-free data augmentation module to prepare data for TDG, a novel pseudo-labeling mechanism to provide reliable supervision for TDA, and a prototype contrastive alignment algorithm to align different domains for achieving TDG, TDA and FA. Extensive experiments on Digits, PACS, and DomainNet demonstrate that RaTP significantly outperforms state-of-the-art works from Continual DA, Source-Free DA, Test-Time/Online DA, Single DG, Multiple DG and Unified DA&DG in TDG, and achieves comparable TDA and FA capabilities.
翻译:在实际应用中,深学习模式往往在非静止环境中运行,目标数据分布的目标数据流随时间推移而不断转移。但是,在在线和离线模式中都有许多域性适应(DA)方法,以提高跨域适应能力。然而,这些DA方法通常只在经过长期适应之后才能提供良好的业绩,在适应之前和期间的新领域表现不佳,特别是在我们称之为“Unfamililier时期”,特别是在域变突然发生和显著的情况下。另一方面,提出了域变(DG)方法,以提高未调整域的模型普及能力。然而,由于严重灾难性地遗忘了所学知识,现有的DG工作对于不断改变域没有效果。为了克服DA和DG在持续域变换期间处理Unfamiliar时期的这些局限性,我们建议RaTP这个框架侧重于改进模型目标域变异(TDG)通用能力,同时在某些领域培训后立即实现有效的目标域变换能力(TDA),在以往域域域变换(FA),RaTP包括一个免费数据扩增模块,用于为TDG、TDG TDG TDG 数据库升级升级的DG 进行数据化和DG 数据库升级升级化提供可靠数据,并实现数据库升级的DG 。