**Inception-ResNet-v2-C** is an image model block for an 8 x 8 grid used in the [Inception-ResNet-v2](https://paperswithcode.com/method/inception-resnet-v2) architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections.

**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.

$$ b_{c} = a_{c}\left(k + \frac{\alpha}{n}\sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta} $$

Where the size is the number of neighbouring channels used for normalization, $\alpha$ is multiplicative factor, $\beta$ an exponent and $k$ an additive factor

Local Response Normalization

ImageNet Classification with Deep Convolutional Neural Networks

Inception-ResNet-v2-C

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

**Pointwise Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) that uses a 1x1 kernel: a kernel that iterates through every single point. This kernel has a depth of however many channels the input image has. It can be used in conjunction with [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution) to produce an efficient class of convolutions known as [depthwise-separable convolutions](https://paperswithcode.com/method/depthwise-separable-convolution).

Image Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)

Source	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com