Compression Technique

Model compression techniques aim to reduce the size and computational cost of large machine learning models, such as Large Language Models (LLMs) and Convolutional Neural Networks (CNNs), without significant performance degradation. Current research focuses on methods like pruning, quantization, and low-rank approximations, often applied in conjunction with techniques like knowledge distillation and optimized data encoding, and evaluated across diverse model architectures including LLMs, CNNs, and point cloud transformers. These advancements are crucial for deploying sophisticated AI models on resource-constrained devices, improving efficiency in federated learning, and mitigating the environmental impact of large-scale AI training and inference.