What is model compression architecture for classification systems?

Model compression reduces model size and computation while preserving classification accuracy.

Compression techniques include pruning, quantization, knowledge distillation, and weight sharing. These are essential for deploying classification models on edge devices or low-latency systems. Distillation trains a smaller student model to mimic a larger teacher model, reducing inference cost significantly.