Contrastive Loss

#deep-learning #loss-in-ml #interview

This is the first loss used for Contrastive Learning
Only one pair of data points are given
- The points are similar or different

Contrastive Loss Formula

$L o s s = (1 - Y) * | x_{i} - x_{j} |^{2} + Y * m a x (0, m - | x_{i} - x_{j} |^{2})$

Here,

Y = 0 if sample is similar
Y = 1 if sample is different
m is a hyper-parameter

This loss mainly minimized the Euclidian Distance between similar points and increases the Euclidian Distance between different points till some limit m.

Related Notes

One Class Classification
AdaGrad
Standardization
SVC
Perplexity