DEJA VU: Continual Model Generalization For Unseen Domains (2301.10418v2)

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.

• The paper introduces RaTP, a novel framework that improves model performance during the unfamiliar period of dynamic domain shifts.
• It employs training-free data augmentation, a Top² pseudo-labeling mechanism, and prototype contrastive alignment to enhance target domain generalization.
• Empirical results on Digits, PACS, and DomainNet demonstrate that RaTP outperforms state-of-the-art methods, reducing the adaptivity gap and mitigating catastrophic forgetting.

Continual Model Generalization for Unseen Domains: An Analysis of "Deja Vu"

The paper "Deja Vu: Continual Model Generalization for Unseen Domains" explores a significant and prevalent challenge in the deployment and effectiveness of deep learning models in dynamic, real-world environments. Specifically, it addresses the issue of target data distribution that continually shifts, creating a period before and during adaptation characterized by poor model performance, labeled as the "Unfamiliar Period." The research provides a framework, named RaTP, aimed at enhancing target domain generalization (TDG) during this Unfamiliar Period and ensuring effective target domain adaptation (TDA) and forgetting alleviation (FA) during continual domain shifts.

Novel Contributions and Methodological Insights

RaTP stands out due to its robust approach to addressing both the continual and sudden nature of domain shifts that complicates real-world deep learning application. It presents a cohesive system that integrates multiple innovative components:

1. Training-Free Data Augmentation: Utilizing a module called RandMix, the framework incorporates data augmentation to craft data distributions that better prepare models for target domain generalization. By leveraging simplified autoencoders, RandMix can introduce diversity and robustness to the training data without necessitating training of the augmentation module itself.
2. Pseudo-Labeling Mechanism: The framework employs a novel $\text{Top}^2$ Pseudo Labeling (T2PL) mechanism designed to provide reliable labels necessary for effective TDA. This approach focuses on confidence-based selection of samples, thereby improving the reliability of pseudo-labels used for training on disjoint target domain instances.
3. Prototype Contrastive Alignment Algorithm: To unify efforts in TDG, TDA, and FA, RaTP uses a prototype-based feature alignment strategy that is augmented by contrastive losses. This allows it to align domain representations around common prototypes effectively, reducing the adaptivity gap and mitigating the catastrophic forgetting of source and prior target domains.

Empirical Validation and Comparative Assessment

RaTP's effectiveness is demonstrated through extensive evaluation across three domain-variant standard datasets: Digits, PACS, and DomainNet. The experiments reveal that RaTP significantly outperforms state-of-the-art methods from multiple distinct areas, including Continual DA, Source-Free DA, Test-Time/Online DA, and Domain Generalization (DG), across key metrics. Specifically, RaTP shows superior TDG capabilities compared to competing frameworks, emphasizing its capacity for strong model initialization and adaptability without extensive a priori domain-specific training data. It also maintains competitive levels of performance in TDA and FA, underscoring its comprehensive approach to continual learning challenges.

Practical and Theoretical Implications

The continual domain shift problem is particularly acute in real-world applications like surveillance systems and medical imaging—domains where rapid yet unforeseen environmental changes are common. In these contexts, the Unfamiliar Period poses substantial obstacles, which RaTP mitigates effectively, broadening its utility to such critical applications. The practical implications of the proposed system extend to any domain requiring robust model performance amidst evolving data distributions. Theoretically, this work enriches the literature by demonstrating how pseudo-labeling and contrastive alignment strategies can be applied in concert to combat and overcome issues of catastrophic forgetting and domain-specific training inertia.

Concluding Thoughts

Overall, "Deja Vu: Continual Model Generalization for Unseen Domains" marks an important step forward in addressing one of the most challenging aspects of real-world deep learning application—dynamic domain shifts. Through its sophisticated integration of data augmentation, pseudo-labeling, and prototype alignment strategies, it not only provides a valuable tool for practitioners but also stimulates further research into continual and adaptive learning paradigms. Future work might explore more adaptive and context-aware variants of RaTP, potentially integrating self-supervised components that could further elevate performance amidst unforeseen domain shifts.