**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.

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.

NVAE Encoder Residual Cell

NVAE: A Deep Hierarchical Variational Autoencoder

Contrastive Multiview Coding

**NT-Xent**, or **Normalized Temperature-scaled Cross Entropy Loss**, is a loss function. Let $\text{sim}\left(\mathbf{u}, \mathbf{v}\right) = \mathbf{u}^{T}\mathbf{v}/||\mathbf{u}|| ||\mathbf{v}||$ denote the cosine similarity between two vectors $\mathbf{u}$ and $\mathbf{v}$. Then the loss function for a positive pair of examples $\left(i, j\right)$ is :

$$ \mathbb{l}\_{i,j} = -\log\frac{\exp\left(\text{sim}\left(\mathbf{z}\_{i}, \mathbf{z}\_{j}\right)/\tau\right)}{\sum^{2N}\_{k=1}\mathcal{1}\_{[k\neq{i}]}\exp\left(\text{sim}\left(\mathbf{z}\_{i}, \mathbf{z}\_{k}\right)/\tau\right)}$$

where $\mathcal{1}\_{[k\neq{i}]} \in ${$0, 1$} is an indicator function evaluating to $1$ iff $k\neq{i}$ and $\tau$ denotes a temperature parameter. The final loss is computed across all positive pairs, both $\left(i, j\right)$ and $\left(j, i\right)$, in a mini-batch.

Source: [SimCLR](https://paperswithcode.com/method/simclr)

Source	Contrastive Multiview Coding
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com