Memory Efficient Adaptation

Memory-efficient adaptation focuses on modifying pre-trained models for new tasks or domains while minimizing memory consumption, a crucial challenge with increasingly large models. Current research explores techniques like low-rank matrix factorization, parameter-efficient fine-tuning (PEFT), and selective parameter updates (e.g., using block coordinate descent or winner-take-all sampling) applied to various architectures including Vision Transformers and Large Language Models (LLMs). These methods aim to improve the efficiency of adapting models to diverse datasets and tasks, impacting fields like natural language processing, computer vision, and potentially enabling on-device learning in resource-constrained environments.