**Decorrelated Batch Normalization (DBN)** 
is a normalization technique which not just centers and scales activations but whitens them. ZCA whitening instead of [PCA](https://paperswithcode.com/method/pca) whitening is employed since PCA whitening causes a problem called *stochastic axis swapping*, which is detrimental to learning.

**GBlock** is a type of [residual block](https://paperswithcode.com/method/residual-block) used in the [GAN-TTS](https://paperswithcode.com/method/gan-tts) text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds
to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for
each BatchNorm instance. 

A GBlock contains two skip connections, the first of which in [GAN](https://paperswithcode.com/method/gan)-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 [convolution](https://paperswithcode.com/method/convolution)
if the number of output channels is different from the input.

GBlock

High Fidelity Speech Synthesis with Adversarial Networks

Decorrelated Batch Normalization

A **Gated Convolutional Network** is a type of language model that combines convolutional networks with a gating mechanism. Zero padding is used to ensure future context can not be seen. Gated convolutional layers can be stacked on top of other hierarchically. Model predictions are then obtained with an [adaptive softmax](https://paperswithcode.com/method/adaptive-softmax) layer.

Source	Decorrelated Batch Normalization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com