Generative Adversarial Networks Projects

Batch normalization

Batch normalization is a technique that normalizes the feature vectors to have no mean or unit variance. It is used to stabilize learning and to deal with poor weight initialization problems. It is a pre-processing step that we apply to the hidden layers of the network and it helps us to reduce internal covariate shift.

Batch normalization was introduced by Ioffe and Szegedy in their 2015 paper, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. This can be found at the following link: https://arxiv.org/pdf/1502.03167.pdf.

The benefits of batch normalization are as follows:

• Reduces the internal covariate shift: Batch normalization helps us to reduce the internal covariate shift ...