What is the difference between model pruning and model compression?

What is the difference between model pruning and model compression?

Model pruning reduces the size of a neural network by removing less important weights, while model compression involves techniques like quantization and encoding to reduce the model's memory footprint.

Can you explain how pruning affects model performance?

Pruning can improve inference speed and reduce memory usage, but if too many weights are removed, it may lead to a drop in accuracy.

Are there specific algorithms used for model pruning?

Yes, common algorithms include weight pruning, neuron pruning, and structured pruning, each targeting different aspects of the model.

How does model compression impact training time?

Model compression techniques can increase training time initially due to the added complexity, but they often lead to faster inference times post-training.

Which method is generally more effective for reducing model size?

It depends on the use case; pruning is effective for reducing size without significant loss in accuracy, while compression can achieve higher reductions but may require more careful tuning.