  Deep neural networks are powerful, massively parameterized machine learning
models that have been shown to perform well in supervised learning tasks.
However, very large amounts of labeled data are usually needed to train deep
neural networks. Several semi-supervised learning approaches have been proposed
to train neural networks using smaller amounts of labeled data with a large
amount of unlabeled data. The performance of these semi-supervised methods
significantly degrades as the size of labeled data decreases. We introduce
Mutual-information-based Unsupervised & Semi-supervised Concurrent LEarning
(MUSCLE), a hybrid learning approach that uses mutual information to combine
both unsupervised and semi-supervised learning. MUSCLE can be used as a
stand-alone training scheme for neural networks, and can also be incorporated
into other learning approaches. We show that the proposed hybrid model
outperforms state of the art on several standard benchmarks, including
CIFAR-10, CIFAR-100, and Mini-Imagenet. Furthermore, the performance gain
consistently increases with the reduction in the amount of labeled data, as
well as in the presence of bias. We also show that MUSCLE has the potential to
boost the classification performance when used in the fine-tuning phase for a
model pre-trained only on unlabeled data.
