shown to work very very well as a pre-training method for classification neural networks you you have an image and it's augmented version and you minimize the inner product or the Euclidean distance between the two versions in the hidden space and the rationale is exactly the same the rationale is that this hidden space of course should be linearly classifiable and so the distance between those should be close and the rationale between having these tasks is that well if I flip the image right if I flip the image