Adapter Learning

Adapter learning is a parameter-efficient fine-tuning technique that modifies pre-trained models for specific tasks by adding small, trainable adapter modules instead of retraining the entire model. Current research focuses on improving adapter architectures (e.g., convolutional, low-rank, and multi-adapter systems) to enhance performance, reduce memory consumption, and address issues like catastrophic forgetting and overfitting. This approach offers significant advantages in resource-constrained environments and enables rapid adaptation of large models to diverse downstream tasks, impacting fields like image segmentation, natural language processing, and recommendation systems.