Dynamic Incremental Regularised Adaptation

Dynamic Incremental Regularized Adaptation (DIRA) focuses on enabling deep learning models to adapt quickly and effectively to new, unseen data distributions using only a small number of samples, thereby avoiding the need for complete retraining. Current research emphasizes regularization techniques to mitigate catastrophic forgetting and improve generalization during this incremental adaptation, often employing graph-based methods or self-supervised learning approaches. This research area is crucial for deploying robust machine learning systems in dynamic environments, particularly in applications like autonomous systems and personalized face recognition, where continuous adaptation to changing conditions is essential.