A wavelet **scattering transform** computes a translation invariant representation, which is stable to deformation, using a deep [convolution](https://paperswithcode.com/method/convolution) network architecture. It computes non-linear invariants with modulus and averaging pooling functions. It helps to eliminate the image variability due to translation and is stable to deformations. 

Image source: [Bruna and Mallat](https://arxiv.org/pdf/1203.1513v2.pdf)

A **Pyramid Pooling Module** is a module for semantic segmentation which acts as an effective global contextual prior. The motivation is that the problem of using a convolutional network like a [ResNet](https://paperswithcode.com/method/resnet) is that, while the receptive field is already larger than the input image, the empirical receptive field is much smaller than the theoretical one especially on high-level layers. This makes many networks not sufficiently incorporate the momentous global scenery prior. 

The PPM is an effective global prior representation that addresses this problem. It contains information with different scales and varying among different sub-regions. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part.

Pyramid Pooling Module

Pyramid Scene Parsing Network

ScatNet

Invariant Scattering Convolution Networks

**SqueezeNet** is a convolutional neural network that employs design strategies to reduce the number of parameters, notably with the use of fire modules that "squeeze" parameters using 1x1 convolutions.

Source	Invariant Scattering Convolution Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com