Implementation of Semi-Supervised and Semi-Weakly Supervised Teacher/Student Learning approaches. [1] Marini N., Otálora S., Müller H., & Atzori M. (2021). “Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on prostate histopathology image classification”, Medical Image Analysis (2021): 102165. Paper link: https://www.sciencedirect.com/science/article/pii/S1361841521002115 Check out the GitHub repository: https://github.com/ilmaro8/Semi_Supervised_Learning