An **Inception Module** is an image model block that aims to approximate an optimal local sparse structure in a CNN. Put simply, it allows for us to use multiple types of filter size, instead of being restricted to a single filter size, in a single image block, which we then concatenate and pass onto the next layer.

**BasicVSR** is a video super-resolution pipeline including optical flow and [residual blocks](https://paperswithcode.com/method/residual-connection). It adopts a typical bidirectional recurrent network. The upsampling module $U$ contains multiple [pixel-shuffle](https://paperswithcode.com/method/pixelshuffle) and convolutions. In the Figure, red and blue colors represent the backward and forward propagations, respectively.  The propagation branches contain only generic components. $S, W$, and $R$ refer to the flow estimation module, spatial warping module, and residual blocks, respectively.

BasicVSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

Inception Module

Going Deeper with Convolutions

**Virtual Batch Normalization** is a normalization method used for training generative adversarial networks that extends batch normalization. Regular [batch normalization](https://paperswithcode.com/method/batch-normalization) causes the output of a neural network for an input example $\mathbf{x}$ to be highly dependent on several other inputs $\mathbf{x}'$ in the same minibatch. To avoid this problem in virtual batch normalization (VBN), each example $\mathbf{x}$ is normalized based on the statistics collected on a reference batch of examples that are chosen once and fixed at the start of training, and on $\mathbf{x}$ itself. The reference batch is normalized using only its own statistics. VBN is computationally expensive because it requires running forward propagation on two minibatches of data, so the authors use it only in the generator network.

Source	Going Deeper with Convolutions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com