Input Adaptation

Input adaptation in machine learning focuses on modifying either model parameters or input data to improve performance on diverse or challenging datasets, particularly when dealing with limited training data or significant domain shifts. Current research explores techniques like dynamic computation allocation, input-conditioned adapters within transformer models, and diffusion-model-based projections to enhance model adaptability and efficiency. These advancements are significant for improving the robustness and resource efficiency of various applications, including natural language processing, computer vision, and medical image analysis, by enabling models to generalize better to unseen data and reduce computational costs.