Classifier Alignment

Classifier alignment focuses on resolving inconsistencies between feature representations and classification layers in machine learning models, particularly within continual learning settings where models learn new tasks sequentially. Current research emphasizes techniques like slow learning rates for feature extractors and post-hoc alignment of classifiers, often employing architectures such as UNet and leveraging concepts like neural collapse to optimize feature-classifier geometry. These advancements aim to mitigate catastrophic forgetting and improve model performance in scenarios with limited data or evolving task distributions, with implications for applications ranging from medical image analysis to robust AI systems.