This method introduces the aggregated dense block within the squeeze excitation block to enhance representation. The squeeze method compresses the input flow and sends it to excitation with dense layers to regain its shape. The paper introduces multiple dense layers stacked side by side, similar to ResNeXt. This learns global representations from the condensed information which enhances the representational power of the network.

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

Inception-A

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

SAENet

Squeeze aggregated excitation network

**LightConv** is a type of [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) for sequential modelling which shares certain output channels and whose weights are normalized across the temporal dimension using a [softmax](https://paperswithcode.com/method/softmax). Compared to self-attention, LightConv has a fixed context window and it determines the importance of context elements with a set of weights that do not change over time steps. LightConv computes the following for the $i$-th element in the sequence and output channel $c$:

$$ \text{LightConv}\left(X, W\_{\text{ceil}\left(\frac{cH}{d}\right),:}, i, c\right) = \text{DepthwiseConv}\left(X,\text{softmax}\left(W\_{\text{ceil}\left(\frac{cH}{d}\right),:}\right), i, c\right) $$

Source	Squeeze aggregated excitation network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com