What are some common compression techniques used in AI?

What are some common compression techniques used in AI?

Some common compression techniques in AI include quantization, pruning, and knowledge distillation.

Can you explain how quantization works?

Quantization reduces the precision of the numbers used in models, which decreases model size and speeds up inference.

What is pruning in the context of AI models?

Pruning involves removing weights or neurons that contribute little to the model's performance, making it more efficient.

How does knowledge distillation help in model compression?

Knowledge distillation transfers knowledge from a larger model (teacher) to a smaller model (student), retaining performance while reducing size.

Are there any trade-offs with these techniques?

Yes, while these techniques reduce size and improve speed, they can also lead to a loss in accuracy if not done carefully.