**Inception-C** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture.

**Margin Rectified Linear Unit**, or **Margin ReLU**, is a type of activation function based on a [ReLU](https://paperswithcode.com/method/relu), but it has a negative threshold for negative values instead of a zero threshhold.

Margin ReLU

A Comprehensive Overhaul of Feature Distillation

Inception-C

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

The **Squeeze-and-Excitation Block** is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is:

- The block has a convolutional block as an input.
- Each channel is "squeezed" into a single numeric value using [average pooling](https://paperswithcode.com/method/average-pooling).
- A dense layer followed by a [ReLU](https://paperswithcode.com/method/relu) adds non-linearity and output channel complexity is reduced by a ratio.
- Another dense layer followed by a sigmoid gives each channel a smooth gating function.
- Finally, we weight each feature map of the convolutional block based on the side network; the "excitation".

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