Adaptive Classification

Adaptive classification focuses on building machine learning models that can dynamically adjust to changes in data distribution, such as shifts in class priors or the emergence of new classes, without requiring complete retraining. Current research explores techniques like test-time adaptation, which modifies model parameters during inference, and retrieval-based methods that leverage previously seen data to classify novel inputs. These advancements are crucial for deploying classifiers in real-world scenarios with non-stationary data streams, improving robustness and reducing the computational cost associated with continuous model retraining in applications ranging from network traffic analysis to service monitoring.