model-compression

Model compression encompasses various strategies aimed at reducing the storage and computational requirements of machine learning models. This includes techniques like pruning, quantization, and knowledge distillation. A common misconception is that compression always results in lower accuracy; however, with careful implementation, performance can remain comparable to larger models.