Parameter Efficient Transfer Learning

Parameter-efficient transfer learning (PETL) focuses on adapting large pre-trained models to new tasks using minimal parameter updates, addressing the computational and storage burdens of full fine-tuning. Current research emphasizes techniques like adapters, prompt tuning, and various other lightweight modules applied to vision transformers (ViTs), diffusion models, and language models, often incorporating strategies to improve cross-modal transfer and multi-task learning. This approach is significant because it enables efficient deployment of powerful models on resource-constrained devices and facilitates rapid adaptation to diverse downstream tasks across various domains, including computer vision, natural language processing, and speech recognition.