Enhancing Deep Neural Network Training with Noise-Based Learning Algorithms
Noise-based learning algorithms, such as node perturbation (NP), can provide an efficient alternative to backpropagation (BP) for training deep neural networks. By combining different NP formulations with a decorrelation mechanism, the performance of NP-based learning can be significantly improved, approaching or even exceeding that of BP in certain contexts.