92 citations · 193 across the 8 of their papers we have counts for
5 papers · 1 filter
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb
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…
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
Angelica I Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb +1
Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing Artificial Intelligence techniqu…
CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
Philip Sellars, Angelica Aviles-Rivero, Carola Bibiane Schönlieb
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 unl…
GraphX Chest X-Ray Classification Under Extreme Minimal Supervision
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the crit…
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has m…