This method introduces several regularization schemes that can be applied to an Autoencoder. To make the model generative *ex-post* density estimation is proposed and consists in fitting a Mixture of Gaussian distribution on the train data embeddings after the model is trained.

**Context Enhancement Module (CEM)** is a feature extraction module used in object detection (specifically, [ThunderNet](https://paperswithcode.com/method/thundernet)) which aims to  to enlarge the receptive field. The key idea of CEM is to aggregate multi-scale local context information and global context information to generate more discriminative features. In CEM, the feature maps from three scales are merged: $C\_{4}$, $C\_{5}$ and $C\_{glb}$. $C\_{glb}$ is the global context feature vector by applying a [global average pooling](https://paperswithcode.com/method/global-average-pooling) on $C\_{5}$. We then apply a 1 × 1 [convolution](https://paperswithcode.com/method/convolution) on each feature map to squeeze the number of channels to $\alpha \times p \times p = 245$.

Afterwards, $C\_{5}$ is upsampled by 2× and $C\_{glb}$ is broadcast so that the spatial dimensions of the three feature maps are
equal. At last, the three generated feature maps are aggregated. By leveraging both local and global context, CEM effectively enlarges the receptive field and refines the representation ability of the thin feature map. Compared with prior [FPN](https://paperswithcode.com/method/fpn) structures, CEM involves only two 1×1 convolutions and a fc layer.

Context Enhancement Module

ThunderNet: Towards Real-time Generic Object Detection

From Variational to Deterministic Autoencoders

NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data.

Description from: [Neural network for graphs: a contextual constructive approach](https://www.meta.org/papers/neural-network-for-graphs-a-contextual/19193509)

Source	From Variational to Deterministic Autoencoders
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com