Continual Test-Time Domain Adaptation (2203.13591v1)

Abstract: Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in non-stationary and continually changing environments where the target domain distribution can change over time. Existing methods, which are mostly based on self-training and entropy regularization, can suffer from these non-stationary environments. Due to the distribution shift over time in the target domain, pseudo-labels become unreliable. The noisy pseudo-labels can further lead to error accumulation and catastrophic forgetting. To tackle these issues, we propose a continual test-time adaptation approach~(CoTTA) which comprises two parts. Firstly, we propose to reduce the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. On the other hand, to avoid catastrophic forgetting, we propose to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the long-term. The proposed method enables the long-term adaptation for all parameters in the network. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models. We demonstrate the effectiveness of our approach on four classification tasks and a segmentation task for continual test-time adaptation, on which we outperform existing methods. Our code is available at \url{https://qin.ee/cotta}.

• The paper introduces CoTTA, a framework that uses weight-averaged and augmentation-averaged pseudo-labels to counter error accumulation during continual adaptation.
• It employs stochastic restoration to preserve key source domain knowledge, effectively preventing catastrophic forgetting.
• The method shows robust performance improvements on benchmarks like CIFAR and Cityscapes, proving its value in dynamic, privacy-sensitive applications.

Insights on Continual Test-Time Domain Adaptation

Continual test-time domain adaptation (CoTTA) presents a significant advancement in addressing the challenges faced by machine perception systems operating in dynamic environments. Traditional domain adaptation methods often necessitate access to both source and target data simultaneously, and they generally assume a static target domain. This paper proposes CoTTA, a framework allowing source pre-trained models to continuously adapt to evolving target domains without requiring access to any source data—a realistic requirement fueled by privacy and legal constraints. CoTTA’s approach is especially pertinent in situations like those encountered by autonomous driving systems, where environmental conditions can vary radically over time.

Focus and Methodology

The paper addresses two primary concerns in continual test-time adaptation: error accumulation and catastrophic forgetting. Error accumulation occurs due to the unreliability of pseudo-labels over time—a challenge intensified by the non-stationary nature of environments. Catastrophic forgetting, on the other hand, relates to the deterioration of the model's pre-trained knowledge through continual adaptation. CoTTA introduces novel strategies to tackle both these challenges:

1. Weight-Averaged and Augmentation-Averaged Pseudo-Labels: CoTTA employs a teacher-student model where the teacher is a weight-averaged version of the current model. This approach helps mitigate error accumulation by providing more reliable pseudo-labels. Additionally, augmentation-averaged pseudo-labels are generated when domain shifts are detected, using augmentations of the target data to average predictions, thus further improving the quality of pseudo-labels.
2. Stochastic Restoration: To prevent catastrophic forgetting, CoTTA stochastically restores some weights to their initial pre-trained values during each adaptation. This method explicitly preserves the source domain knowledge, ensuring that critical information is retained while allowing adaptation to novel test conditions.

Results and Implications

The efficacy of CoTTA was evaluated on several benchmark tasks, including image classification on CIFAR10-to-CIFAR10C, CIFAR100-to-CIFAR100C, and ImageNet-to-ImageNet-C, as well as semantic segmentation tasks between Cityscapes and ACDC. The results demonstrated a consistent performance improvement over baseline methods and highlighted CoTTA’s robustness in neural architecture versatility, including transformer-based models. Notably, CoTTA improved the continual adaptation performance on both classification and segmentation benchmarks, validating its practical applicability.

These results indicate that the proposed approach can be seamlessly integrated into existing pipelines relying on off-the-shelf models, without necessitating source data access. This approach is vital for privacy-sensitive applications and scenarios where access to source data is impractical or prohibited.

Future Developments and Theoretical Considerations

The proposed CoTTA framework marks notable progress in the domain of continual learning and domain adaptation, traversing the complexities introduced by non-static test environments. Future research could build upon this work by exploring its application to more diverse tasks and further refining pseudo-labeling techniques, possibly incorporating advanced confidence metrics or adaptive augmentation strategies.

Theoretically, CoTTA underscores the utility of teacher-student frameworks in mitigating error accumulation, positioning weight-averaging not just as a stabilization technique but as a cornerstone of robust pseudo-labeling strategies in evolving domains. Furthermore, the stochastic restoration mechanism presents an interesting avenue for further exploration, perhaps inspiring new anchor-based methods for preserving crucial model parameters.

In conclusion, this work provides a foundation for more adaptive and resilient AI systems capable of functioning efficiently amidst constant domain shifts. The exploration of CoTTA continues to expand the boundaries of adaptive learning, promising enhanced adaptation capabilities without compromising on model integrity or pre-trained knowledge.