InfoNCE, where NCE stands for NoiseContrastive Estimation, is a type of contrastive loss function used for selfsupervised learning.
Given a set $X = ${$x_{1}, \dots, x_{N}$} of $N$ random samples containing one positive sample from $p\left(x_{t+k}c_{t}\right)$ and $N − 1$ negative samples from the 'proposal' distribution $p\left(x_{t+k}\right)$, we optimize:
$$ \mathcal{L}_{N} =  \mathbb{E}_{X}\left[\log\frac{f_{k}\left(x_{t+k}, c_{t}\right)}{\sum_{x_{j}\in{X}}f_{k}\left(x_{j}, c_{t}\right)}\right] $$
Optimizing this loss will result in $f_{k}\left(x_{t+k}, c_{t}\right)$ estimating the density ratio, which is:
$$ f_{k}\left(x_{t+k}, c_{t}\right) \propto \frac{p\left(x_{t+k}c_{t}\right)}{p\left(x_{t+k}\right)} $$
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