Mitigating the Bias in the Model for Continual Test-Time Adaptation

Abstract: Continual Test-Time Adaptation (CTA) is a challenging task that aims to adapt a source pre-trained model to continually changing target domains. In the CTA setting, a model does not know when the target domain changes, thus facing a drastic change in the distribution of streaming inputs during the test-time. The key challenge is to keep adapting the model to the continually changing target domains in an online manner. We find that a model shows highly biased predictions as it constantly adapts to the chaining distribution of the target data. It predicts certain classes more often than other classes, making inaccurate over-confident predictions. This paper mitigates this issue to improve performance in the CTA scenario. To alleviate the bias issue, we make class-wise exponential moving average target prototypes with reliable target samples and exploit them to cluster the target features class-wisely. Moreover, we aim to align the target distributions to the source distribution by anchoring the target feature to its corresponding source prototype. With extensive experiments, our proposed method achieves noteworthy performance gain when applied on top of existing CTA methods without substantial adaptation time overhead.

• The paper introduces a novel bias mitigation method using EMA prototypical loss and prototype matching for improved continual adaptation.
• It aligns target and source distributions to enhance class-wise feature clustering and boosts accuracy on benchmarks like ImageNet-C and CIFAR100-C.
• The approach integrates seamlessly with existing CTA methods, achieving robust performance improvements with minimal computational overhead.

Mitigating Bias in Continual Test-Time Adaptation for Improved Model Performance

Introduction to the Paper's Context and Contribution

In the ever-evolving domain of deep learning deployment, models often encounter streaming data whose distribution differs significantly from that of the training set. This disparity necessitates mechanisms for continual test-time adaptation (CTA) to maintain model accuracy over time. A particular challenge in CTA is the model's tendency to exhibit bias towards specific classes, leading to overconfident and inaccurate predictions. Addressing this, the research introduces a novel method designed to mitigate such biases, employing class-wise target prototypes and aligning target distributions to source distributions through prototype matching. The approach is seamlessly integrable with existing CTA methods, enhancing performance without notable adaptation time overhead.

Deep Dive into the Methodology

The proposed method comprises two primary components: the exponential moving average (EMA) target domain prototypical loss and source distribution alignment via prototype matching. The EMA prototypical loss leverages reliable target samples to continuously update each class prototype, which helps in class-wise clustering of the target features. This strategy effectively captures the changing target distributions and aims to prevent an undue bias towards current target distributions. Furthermore, to anchor the target data distribution closely to the source distribution, the paper proposes minimizing the distance between the target feature and its corresponding source prototype. This approach stands out for its simplicity, deviating from the complex metrics such as KL-Divergence typically employed for domain alignment.

Empirical Evidence and Observations

Extensive experiments validate the effectiveness of the proposed method on standard CTA benchmarks like ImageNet-C and CIFAR100-C. Notably, when applied atop existing methods, significant performance improvements are observed with minimal adaptation time overhead. The method not only enhances the average accuracy across various classes and conditions but also corrects the model's calibration, reducing overconfidence in predictions. Moreover, the research convincingly demonstrates the method's robustness to variations in the target domain's presentation order and its scalability across different batch sizes.

Theoretical and Practical Implications

On the theoretical front, this paper contributes to the understanding of bias in continual learning scenarios and offers a viable strategy for its mitigation. Practically, the ease of integrating the proposed method with existing approaches makes it highly relevant for real-world applications that require continual adaptation to changing data distributions. Furthermore, the negligible increase in adaptation time underscores the method's feasibility for deployment in time-sensitive applications.

Speculative Look into the Future

Given the promising results, future work could explore several directions. These include a more granular understanding of the mechanisms driving the observed improvements and extending the methodology to address other forms of bias that may arise in continual learning scenarios. Furthermore, investigating the interplay between the proposed method and different model architectures or types of streaming data could yield insights into achieving even more robust and versatile test-time adaptation strategies.

Conclusions

In summary, this research makes a significant contribution to the field of continual learning by addressing the issue of biased predictions during test-time adaptation. Through a simplified yet effective approach, it not only enhances model performance across a spectrum of real-world conditions but also does so with minimal computational overhead. Its compatibility with existing continual adaptation methods further underscores its practical utility, marking a step forward in the development of adaptable, fair, and accurate machine learning models.