Classifier Re Training

Classifier retraining focuses on improving the performance and robustness of machine learning models by iteratively refining their classification components. Current research emphasizes addressing challenges like performative shifts (where model deployment alters data distributions), long-tailed recognition (imbalanced class distributions), and adversarial attacks. Methods range from simple logits retargeting and regularization techniques to more complex approaches involving federated learning and the transfer of knowledge from vision-language models. These advancements aim to enhance model accuracy, fairness, and resilience in diverse real-world applications.