Deep Learning, Artificial Intelligence, Convolutional Network, Semi Supervised Learning, Image Classification.
on MNIST using all training data without data augmentation. Combination of both loss functions reduces the error rate to 0.55% which is the state-of-the-art for the task of MNIST with 100 labeled samples at that moment. It can be seen that a close accuracy is achieved by using only 100 labeled