The **NVAE Encoder Residual Cell** is a [residual connection](https://paperswithcode.com/method/residual-connection) block used in the [NVAE](https://paperswithcode.com/method/nvae) architecture for the encoder. It applies two series of BN-[Swish](https://paperswithcode.com/method/swish)-Conv layers without changing the number of channels.

A **R(2+1)D** convolutional neural network is a network for action recognition that employs [R(2+1)D](https://paperswithcode.com/method/2-1-d-convolution) convolutions in a [ResNet](https://paperswithcode.com/method/resnet) inspired architecture. The use of these convolutions over regular [3D Convolutions](https://paperswithcode.com/method/3d-convolution) reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.

R(2+1)D

A Closer Look at Spatiotemporal Convolutions for Action Recognition

NVAE Encoder Residual Cell

NVAE: A Deep Hierarchical Variational Autoencoder

**Contrastive Multiview Coding (CMC)** is a self-supervised learning approach, based on [CPC](https://paperswithcode.com/method/contrastive-predictive-coding), that  learns representations that capture information shared between multiple sensory views. The core idea is to set an anchor view and the sample positive and negative data points from the other view and maximise agreement between positive pairs in learning from two views. Contrastive learning is used to build the embedding.

Source	NVAE: A Deep Hierarchical Variational Autoencoder
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com