Contrastive Learning?

Contrastive learning is a type of unsupervised learning technique used in machine learning and deep learning. The main idea behind contrastive learning is to learn representations by contrasting similar and dissimilar pairs of data samples. In contrastive learning, the model learns to differentiate between two similar data samples and two dissimilar data samples.

The process of contrastive learning involves creating pairs of data samples, where one sample is considered as a positive example and the other one as a negative example. The model tries to learn a representation that maps the positive examples closer together in the embedding space while pushing the negative examples further apart.

Contrastive learning has been used in various applications such as image recognition, natural language processing, and speech recognition. It has shown promising results in improving the accuracy and robustness of these models.