Network Reduction

Network reduction aims to simplify complex neural networks while preserving essential functionality, addressing challenges in verification, deployment, and training efficiency. Current research focuses on methods like eliminating redundant neurons, identifying and aggregating coherent clusters within network structures, and strategically modifying connections between layers (e.g., in DenseNets), often leveraging concepts from Markov chain lumpability. These techniques improve verification speed, reduce computational demands for embedded systems, and enhance training performance, particularly for resource-constrained applications or large datasets.