CompNet: Neural networks growing via the compact network morphism
This work addresses the issue of wasteful computational resources in training multiple neural network architectures in parallel, offering an incremental improvement over existing network morphism approaches.
The paper tackles the problem of inefficient neural network training by introducing CompNet, a method that morphs a trained network into a deeper one while preserving functionality and using compact added layers, achieving faster convergence without retraining from scratch.
It is often the case that the performance of a neural network can be improved by adding layers. In real-world practices, we always train dozens of neural network architectures in parallel which is a wasteful process. We explored $CompNet$, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact. The work of the paper makes two contributions: a). The modified network can converge fast and keep the same functionality so that we do not need to train from scratch again; b). The layer size of the added layer in the neural network is controlled by removing the redundant parameters with sparse optimization. This differs from previous network morphism approaches which tend to add more neurons or channels beyond the actual requirements and result in redundance of the model. The method is illustrated using several neural network structures on different data sets including MNIST and CIFAR10.