Fuzzy Rule-Based Test-Time Adaptation for Class Imbalance in Dynamic Scenarios

Test-time adaptation (TTA) leverages real-time updates with test samples to effectively address distribution shifts. Previous TTA methods have achieved stable results in scenarios with continual distribution change through techniques such as teacher-student model distillation and image augmentation. However, these test-time adaptation methods are primarily designed for scenarios with balanced class distributions. Yet, in practical applications, having imbalanced classes is more common, which can present a significant challenge. In test data streams characterized by distribution shifts and class imbalances, continual updates can cause the model to become biased towards extreme samples, reducing overall performance. There have only been a few attempts to reduce this influence—generally by sampling from imbalanced test streams to create a more balanced distribution. However, even with these strategies, the models still suffer from biases toward certain categories when the class imbalance is extreme. To overcome the influence of class imbalance in dynamic settings, we propose a Fuzzy Rule-based Test-Time Adaptation method (FTTA). The method incorporates fuzzy rules that allow weights to be assigned to each sample based on its degree of membership in different categories, which helps alleviate the problem of neglecting minor categories in traditional hard categorization methods. By using fuzzy rules, FTTA guides the model to select particular updates. Thus, it prevents the model from excessively favoring specific samples during iterative updates and maintains balance throughout the update process, improving overall accuracy and robustness. Experiments with five datasets in four-class imbalanced continual dynamic scenarios validate the proposed method with FTTA consistently achieving state-of-the-art performance.