Semi-supervised learning is a machine learning approach that allows us to leverage both labeled and unlabeled data during the training process. In this project, I explore 3 different semi-supervised learning techniques which may help improve the performance of an image classifier. I will demonstrate how each technique can be incorporated into a typical training system, and provide my findings and thoughts on the method.