Feature Drift

Feature drift, the undesirable change in the relationship between input features and target variables over time, poses a significant challenge in machine learning, particularly in continual learning scenarios and applications involving evolving data distributions. Current research focuses on mitigating feature drift through techniques like drift compensation, often implemented using neural networks with specialized modules (e.g., feature compensation modules, gated class-attention mechanisms) or by leveraging adversarial perturbations to track prototype movement. Addressing feature drift is crucial for improving the robustness and reliability of machine learning models across various domains, including cybersecurity, medical image analysis, and robotics, where data characteristics can change dynamically.