Continual Fine Tuning

Continual fine-tuning focuses on adapting pre-trained models to new tasks sequentially, without catastrophic forgetting of previously learned information. Current research emphasizes mitigating this forgetting through techniques like low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), and novel regularization methods applied to various architectures including transformers and convolutional neural networks. This area is crucial for efficient model updates in resource-constrained environments and enables personalized and adaptive AI systems across diverse applications like healthcare and autonomous driving, reducing the need for extensive retraining.