Abstract
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.
Abstract (translated)
在实际应用中,深度学习模型经常运行在时间不断变化的目标数据分布的非稳定环境中。在在线和离线模式下,已经有许多域适应(DA)方法来提高跨域适应能力。然而,这些DA方法通常只有在长时间的适应之后才能提供良好的性能,并且在适应过程中对新 domains 的表现很差 - 我们称之为“不熟悉的时期”,特别是当域变化突然且显著时。另一方面,已经提出了域泛化(DG)方法,以改善未适应域的模型泛化能力。然而,现有的DG方法对于不断更改的域来说效果不佳,因为严重的灾难性遗忘了学习知识。为了克服在持续域更改期间处理不熟悉的时期 DA 和 DG 的局限性,我们提出了RaTP,一个框架,专注于改善模型的目标域泛化能力(TDG)能力,同时也在训练某些域后实现有效的目标域适应能力(TDA)能力和过去域的遗忘缓解能力(FA)能力。RaTP 包括一个无需训练的数据增强模块来准备 TDG 的数据,一个新颖的伪标签机制来提供可靠的TDA监督,以及一个原型对比对齐算法来对齐不同域以实现 TDG、TDA 和FA。对digits、PACS 和 DomainNet进行了广泛的实验,表明RaTP 在 TDG 方面显著优于现有的持续 DA、无源 DA、测试时/在线 DA、单 DG、多 DG 和统一的目标域适应与域泛化方法,并实现了与 TDA 和FA 能力相当的 TDG 和遗忘缓解能力。
URL
https://arxiv.org/abs/2301.10418
