Knowledge Transfer in Self Supervised Learning

Self Supervised Learning is an interesting research area where the goal is to learn rich representations from unlabeled data without any human annotation. This can be achieved by creatively formulating a problem such that you use parts of the data itself as labels and try to predict that. Such formulations are called pretext tasks. For example, you can setup a pretext task to predict the color version of the image given the grayscale version. Similarly, you could remove a part of the image and train a model to predict the part from the surrounding.