LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification

Understand

Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect.

• Recent developments in deep semi-supervised classification have reached unprecedented performance and the gap between supervised and semi-supervised learning is ever-decreasing.
• This improvement in performance has been based on the inclusion of numerous technical tricks, strong augmentation techniques and costly optimisation schemes with multi-term loss functions.
• We propose a new framework, LaplaceNet, for deep semi-supervised classification that has a greatly reduced model complexity.