Pruning
term_id: pruning
Category: training_techniques
Definition
Pruning involves identifying and eliminating neurons, connections, or filters in a neural network that contribute minimally to the output accuracy. By removing these redundant elements, the model becomes smaller and faster to execute without significantly compromising performance. This technique is crucial for deploying deep learning models on resource-constrained devices like mobile phones or embedded systems.
Summary
A model compression technique that removes redundant or less significant parameters to reduce size and improve inference speed.
Key Concepts
- model compression
- redundancy removal
- inference acceleration
- sparsity
Use Cases
- Mobile AI deployment
- Edge computing optimization
- Reducing cloud inference costs