Howtosignalprocessing
- What is the difference between normalization and batch normalization?
- What is spectral normalization?
- Why do we need spectral normalization?
- Why group normalization is better than batch normalization?
What is the difference between normalization and batch normalization?
Normalization is a procedure to change the value of the numeric variable in the dataset to a typical scale, without misshaping contrasts in the range of value. Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini-batch.
What is spectral normalization?
Spectral Normalization is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.
Why do we need spectral normalization?
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs).
Why group normalization is better than batch normalization?
GN is better than IN as GN can exploit the dependence across the channels. It is also better than LN because it allows different distribution to be learned for each group of channels. When the batch size is small, GN consistently outperforms BN.
