334 citations · 1.3k across the 54 of their papers we have counts for
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Towards Robust Learning with Different Label Noise Distributions
Diego Ortego, Eric Arazo, Paul Albert +2
Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important step towards preventing performance degradations in Convolutional Neural Networ…
End-to-End Conditional GAN-based Architectures for Image Colourisation
Marc Górriz, Marta Mrak, Alan F. Smeaton +1
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly ma…
Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images
Jaynal Abedin, Joseph Antony, Kevin McGuinness +4
Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments accordin…
Assessing Knee OA Severity with CNN attention-based end-to-end architectures
Marc Górriz, Joseph Antony, Kevin McGuinness +2
This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which i…
Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity
Joseph Antony, Kevin McGuinness, Kieran Moran +1
This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the a…
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Eric Arazo, Diego Ortego, Paul Albert +2
Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of…
Simple vs complex temporal recurrences for video saliency prediction
Panagiotis Linardos, Eva Mohedano, Juan Jose Nieto +3
This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal…
On guiding video object segmentation
Diego Ortego, Kevin McGuinness, Juan C. SanMiguel +3
This paper presents a novel approach for segmenting moving objects in unconstrained environments using guided convolutional neural networks. This guiding process relies on foregrou…
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert +2
Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there…