Deep Neural Network models based on convolutions need a large dataset to be trained successfully. In the Computer Vision context this implies that a lot of labeled images have to be collected in order to obtain a model with good performances. A trained model is then often unusable when exploited in a visual domain which is different from the training one; moreover the data collection and labeling process can be physically or economically impossible in some visual domains. From these considerations the need to develop algorithms robust to visual domain shifts. ?? Self supervised tasks have shown a great potential as a strategy to learn useful features from unlabeled images.