Much like the language models we can train computer vision models using similar approaches. To make things work in computer vision, we need to formulate the learning tasks such that the underlying model (a deep neural network) is able to make sense of the semantic information present in vision data. One such task is to a model to contrast between two different versions of the same image. The hope is that in this way the model will have learn representations where the similar images are grouped as together possible while the dissimilar images are further away.