Search Results for author:
Found 8 papers, 3 papers with code
LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification
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.
GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease.
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification
In this work, we propose an all-in-one framework for deep semi-supervised classification focusing on graph based approaches, which up to our knowledge it is the first time that an approach with minimal labels has been shown to such an unprecedented scale with medical data.
CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification
Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation.
GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision
The task of classifying X-ray data is a problem of both theoretical and clinical interest.
Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
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.
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.
