Contrastive objective

A contrastive objective in machine learning is a loss function used to train models to learn the difference between two or more states via their distance to each other in a given latent space. The goal is to teach the model to distinguish between different states by providing positive feedback when a correct state is predicted and negative feedback when a wrong one. Contrastive objectives can be used for a variety of tasks, such as image recognition and natural language processing. By providing a clear signal of what the correct output should be, contrastive objectives can help to improve the accuracy of machine learning models.