Search results with tag "Batch normalization"
How Does Batch Normalization Help Optimization?
proceedings.neurips.ccBatch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm’s effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling
FiLM: Visual Reasoning with a General Conditioning Layer
arxiv.orgFigure 3: The FiLM generator (left), FiLM-ed network (mid-dle), and residual block architecture (right) of our model. Adam (Kingma and Ba 2015) (learning rate 3e 4), weight decay (1e 5), batch size 64, and batch normalization and ReLU throughout FiLM-ed network.
Batch Normalization: Accelerating Deep Network Training …
arxiv.orgarXiv:1502.03167v3 [cs.LG] 2 Mar 2015 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe
Batch Normalization: Accelerating Deep Network Training …
proceedings.mlr.presswork parameters during training. To improve the training, we seek to reduce the internal covariate shift. By fixing the distribution of the layer inputs x as the training pro-gresses, we expect to improve the training speed. It has been long known (LeCun et al.,1998b;Wiesler & Ney, 2011) that the network training converges faster if its in-