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)

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.

From Variational to Deterministic Autoencoders

NN4G

**Detailed Expression Capture and Animation**, or **DECA**, is a model for 3D face reconstruction that is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image. A detail-consistency loss is used to disentangle person-specific details and expression-dependent wrinkles. This disentanglement allows us to synthesize realistic person-specific wrinkles by controlling expression parameters while keeping person-specific details unchanged.

Year	2009
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