Task Agnostic Compression

Task-agnostic compression aims to reduce the size of large machine learning models without sacrificing performance across diverse applications, unlike task-specific methods which optimize for a single use case. Current research focuses on developing efficient compression techniques, often employing neural network pruning, variational autoencoders, or implicit neural representations, tailored to various model architectures including large language models, vision-language models, and those used in robotics. This research is crucial for deploying large models on resource-constrained devices and improving the efficiency of various applications, ranging from image processing and natural language understanding to robotic control. The ultimate goal is to achieve significant model size reduction while maintaining generalizability and performance across multiple tasks.