In recent years, the size of Deep Learning models has increased. This trend has been allowed by the advancements in this research area and the development of the hardware targeted for its use. It has led to better results overall. Bigger models, having more parameters, can adapt better to the given task than their smaller counterparts. However, small models are helpful in many real-life scenarios, if not mandatory. They are needed for energy consumption constraints, speed requirements, and hardware limitations. The lottery ticket hypothesis states that a trained model may contain a sub-network whose performance would be similar to the one of the whole model.