**ENet** is a semantic segmentation architecture which utilises a compact encoder-decoder architecture. Some design choices include:

1. Using the [SegNet](https://paperswithcode.com/method/segnet) approach to downsampling y saving indices of elements chosen in max
pooling layers, and using them to produce sparse upsampled maps in the decoder.
2.  Early downsampling to optimize the early stages of the network and reduce the cost of processing large input frames. The first two blocks of ENet heavily reduce the input size, and use only a small set of feature maps. 
3. Using PReLUs as an activation function
4. Using dilated convolutions 
5. Using Spatial [Dropout](https://paperswithcode.com/method/dropout)

**GMVAE**, or **Gaussian Mixture Variational Autoencoder**, is a stochastic regularization layer for [transformers](https://paperswithcode.com/methods/category/transformers). A GMVAE layer is trained using a 700-dimensional internal representation of the first MLP layer. For every output from the first MLP layer, the GMVAE layer first computes a latent low-dimensional representation sampling from the GMVAE posterior distribution to then provide at the output a reconstruction sampled from a generative model.

GMVAE

Regularizing Transformers With Deep Probabilistic Layers

ENet

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

**MPRNet** is a multi-stage progressive image restoration architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, the model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, a per-pixel adaptive design is introduced that leverages in-situ supervised attention to reweight the local features.

Source	ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com