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

• URL: http://arxiv.org/abs/2106.04527v1
• Date: Tue, 8 Jun 2021 17:09:28 GMT
• Title: LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification
• Authors: Philip Sellars and Angelica I. Aviles-Rivero and Carola-Bibiane Sch\"onlieb
• Abstract summary: Recent developments in deep semi-supervised classification have reached unprecedented performance. We propose a new framework, LaplaceNet, for deep semi-supervised classification that has a greatly reduced model complexity. Our model outperforms state-of-the-art methods for deep semi-supervised classification, over several benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We utilise a hybrid energy-neural network where graph based pseudo-labels, generated by minimising the graphical Laplacian, are used to iteratively improve a neural-network backbone. Our model outperforms state-of-the-art methods for deep semi-supervised classification, over several benchmark datasets. Furthermore, we consider the application of strong-augmentations to neural networks theoretically and justify the use of a multi-sampling approach for semi-supervised learning. We demonstrate, through rigorous experimentation, that a multi-sampling augmentation approach improves generalisation and reduces the sensitivity of the network to augmentation.

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