  In many real-world scenarios, labeled data for a specific machine learning
task is costly to obtain. Semi-supervised training methods make use of
abundantly available unlabeled data and a smaller number of labeled examples.
We propose a new framework for semi-supervised training of deep neural networks
inspired by learning in humans. "Associations" are made from embeddings of
labeled samples to those of unlabeled ones and back. The optimization schedule
encourages correct association cycles that end up at the same class from which
the association was started and penalizes wrong associations ending at a
different class. The implementation is easy to use and can be added to any
existing end-to-end training setup. We demonstrate the capabilities of learning
by association on several data sets and show that it can improve performance on
classification tasks tremendously by making use of additionally available
unlabeled data. In particular, for cases with few labeled data, our training
scheme outperforms the current state of the art on SVHN.
