**DVD-GAN DBlock** is a residual block for the discriminator used in the [DVD-GAN](https://paperswithcode.com/method/dvd-gan) architecture for video generation. Unlike regular [residual blocks](https://paperswithcode.com/method/residual-block), [3D convolutions](https://paperswithcode.com/method/3d-convolution) are employed due to the application to multiple frames in a video.

A **ResNest** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks Split-Attention blocks. The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape.  For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes:  $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided [convolution](https://paperswithcode.com/method/convolution) or combined convolution-with-pooling.

ResNeSt

ResNeSt: Split-Attention Networks

DVD-GAN DBlock

Adversarial Video Generation on Complex Datasets

The **Cosine Linear Unit**, or **CosLU**, is a type of activation function that has trainable parameters and uses the cosine function.

$$CosLU(x) = (x + \alpha \cos(\beta x))\sigma(x)$$

Source	Adversarial Video Generation on Complex Datasets
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com