EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization (2303.01904v4)

Abstract: This paper presents a simple yet effective approach that improves continual test-time adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge devices with limited memory, so reducing memory is crucial but has been overlooked in previous TTA studies. In addition, long-term adaptation often leads to catastrophic forgetting and error accumulation, which hinders applying TTA in real-world deployments. Our approach consists of two components to address these issues. First, we present lightweight meta networks that can adapt the frozen original networks to the target domain. This novel architecture minimizes memory consumption by decreasing the size of intermediate activations required for backpropagation. Second, our novel self-distilled regularization controls the output of the meta networks not to deviate significantly from the output of the frozen original networks, thereby preserving well-trained knowledge from the source domain. Without additional memory, this regularization prevents error accumulation and catastrophic forgetting, resulting in stable performance even in long-term test-time adaptation. We demonstrate that our simple yet effective strategy outperforms other state-of-the-art methods on various benchmarks for image classification and semantic segmentation tasks. Notably, our proposed method with ResNet-50 and WideResNet-40 takes 86% and 80% less memory than the recent state-of-the-art method, CoTTA.

EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization

The paper "EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-distilled Regularization" addresses significant challenges associated with test-time adaptation (TTA) for deep neural networks, primarily focusing on memory efficiency and long-term adaptation stability. This research is particularly pertinent for real-world applications on edge devices which often have constrained memory resources.

Overview

The authors propose two main components to enhance TTA: 1) a novel architectural setup consisting of meta networks that facilitate adaptation without the need for large intermediate activations, and 2) a self-distilled regularization mechanism aimed at preserving the source domain knowledge while mitigating error accumulation during long-term adaptation.

Architectural Innovation

1. Memory-efficient Architecture:
   • The architecture involves adding lightweight meta networks to pre-trained networks, thus enabling adaptation with minimal memory usage. Meta networks are crafted as small modules that can adaptively handle domain shifts without requiring the substantial computational overhead associated with retraining full models. By freezing the original networks during the test-time adaptation phase, the authors can discard large intermediate activations, resulting in an 86% reduction in memory usage compared to the state-of-the-art CoTTA system.
2. Model Partitioning:
   • The authors strategically partition the encoder into smaller blocks and attach meta networks, focusing more adaptation effort on the shallow layers—a choice guided by the observation that adjusting lower-level features can significantly improve classification performance in domain-shift scenarios.

Self-Distilled Regularization

Self-distilled regularization ensures adapted models maintain a semblance of their source domain accuracy while adapting to new data distributions. This regularization uses the frozen original networks as a reference during adaptation, penalizing meta network outputs that deviate excessively from the outputs of the frozen networks. This constraint helps prevent catastrophic forgetting, a common issue in continual adaptation scenarios, as well as minimizing error accumulation by stabilizing predictions despite noisy unsupervised losses.

Performance Evaluation

The authors comprehensively evaluate EcoTTA across multiple image classification tasks—CIFAR10/100-C and ImageNet-C—and semantic segmentation tasks, demonstrating enhanced stability and memory efficiency. Notable results include outperforming several baselines based on average error rates and memory consumption, showcasing EcoTTA's ability to operate effectively in diverse conditions with low memory overhead.

Practical and Theoretical Implications

EcoTTA offers practical advantages for deploying AI models on memory-constrained devices, making it particularly suited for applications such as autonomous driving or mobile health systems where real-time adaptation to dynamic environments is required. The paper further highlights the theoretical significance of regularization in continual adaptation tasks, suggesting future research directions that could explore more sophisticated regularization techniques or meta-network architectures to handle even more complex domain shifts.

Future Developments

The findings in this paper open several avenues for future research, including refinements in meta network designs, exploration of adaptive learning rates during regularization, and integration with more complex hierarchical or attention-based models for domain shifts. Additionally, the authors advocate for more empirical work on the scalability of test-time adaptation mechanisms, particularly as edge devices become increasingly central to AI deployment strategies.

In conclusion, this paper presents a nuanced perspective on the challenges and solutions in test-time adaptation, offering a robust framework that balances adaptive performance with memory efficiency—a vital consideration for edge computing applications.