Increasing Training Stability for Deep CNNS
Pierre Gillot, Jenny Benois‐Pineau, Akka Zemmari, Yurii E. Nesterov · 2018
In the present work, we investigate the possibility to expand existing Deep Learning solvers in order to improve the quality and stability of the training. We propose different new solvers, all of them based on the filtering of the neural network parameters, and experimentally prove that properly tuning their respective hyper-parameters leads to a clear improvement of the training and validation results, in quality and in stability.