  Given the potential difficulties in obtaining large quantities of labelled
data, many works have explored the use of deep semi-supervised learning, which
uses both labelled and unlabelled data to train a neural network architecture.
The vast majority of SSL approaches focus on implementing the low-density
separation assumption or consistency assumption, the idea that decision
boundaries should lie in low density regions. However, they have implemented
this assumption by making local changes to the decision boundary at each data
point, ignoring the global structure of the data. In this work, we explore an
alternative approach using the global information present in the clustered data
to update our decision boundaries. We propose a novel framework, CycleCluster,
for deep semi-supervised classification. Our core optimisation is driven by a
new clustering based regularisation along with a graph based pseudo-labels and
a shared deep network. Demonstrating that direct implementation of the cluster
assumption is a viable alternative to the popular consistency based
regularisation. We demonstrate the predictive capability of our technique
through a careful set of numerical results.
