Adaptation Method

Adaptation methods in machine learning focus on efficiently modifying pre-trained models for new tasks or domains, minimizing computational cost and preventing catastrophic forgetting. Current research emphasizes parameter-efficient techniques like adapters and low-rank adaptations (LoRA), applied to various architectures including large language models (LLMs), vision-language models (VLMs), and vision transformers, often incorporating strategies to improve robustness against data corruption. These advancements are crucial for deploying large models in resource-constrained environments and enhancing their generalizability and reliability across diverse applications.