Task Agnostic Distillation

Task-agnostic distillation aims to compress large, powerful machine learning models (like transformers and convolutional neural networks) into smaller, more efficient versions without sacrificing significant performance across various tasks. Current research focuses on optimizing distillation techniques for different model architectures, including exploring various knowledge transfer methods (e.g., hidden state, attention mechanism transfer) and addressing challenges like distribution mismatch between teacher and student models. This research is crucial for deploying advanced AI models on resource-constrained devices and improving the accessibility and scalability of machine learning applications.