Langevin algorithms for very deep Neural Networks with application to image classification
This addresses the problem of local traps in deep neural network training for researchers and practitioners in machine learning, offering incremental improvements over existing methods.
The paper tackles the challenge of training very deep neural networks by comparing preconditioned Langevin algorithms to non-Langevin methods, finding that Langevin algorithms provide greater gains as network depth increases, with a new Layer Langevin algorithm showing benefits for deep residual architectures in image classification.
Training a very deep neural network is a challenging task, as the deeper a neural network is, the more non-linear it is. We compare the performances of various preconditioned Langevin algorithms with their non-Langevin counterparts for the training of neural networks of increasing depth. For shallow neural networks, Langevin algorithms do not lead to any improvement, however the deeper the network is and the greater are the gains provided by Langevin algorithms. Adding noise to the gradient descent allows to escape from local traps, which are more frequent for very deep neural networks. Following this heuristic we introduce a new Langevin algorithm called Layer Langevin, which consists in adding Langevin noise only to the weights associated to the deepest layers. We then prove the benefits of Langevin and Layer Langevin algorithms for the training of popular deep residual architectures for image classification.