Info NCE loss is a popular contrastive loss function that optimizes the log probability of classifying the positive sample correctly given a set of negative samples and one positive example - ![[Pasted image 20221121002421.png|400]] - where \(f(x,c)\) is a scoring function that takes the anchor sample \(x\) and a context vector \(c\) (indicating the class of the anchor) and outputs a score indicating the likelihood that \(x\) is from \(c\) \(f(x_{pos},c) = \frac{p(x_{pos}|c)}{p(x_{pos})}\) , where \(p(x_{pos}|c)\) is approximated by our model through sigmoid or softmax

Ideas¶

I could try this score out for #Project_Barretts