What is model compression?

Model compression refers to techniques for reducing the size of machine learning models, making them more efficient to store, transmit, and deploy, particularly on resource-constrained devices like mobile phones or embedded systems. Common methods include pruning (removing unimportant connections), quantization (reducing the precision of weights), knowledge distillation (training a smaller model to mimic a larger, more complex model), and weight sharing. These techniques aim to minimize the computational cost and memory footprint of models while preserving acceptable levels of accuracy.