Search Results for author: Tomas E. Ward

Found 13 papers, 4 papers with code

Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset

no code implementations14 Sep 2023 Iya Chivileva, Philip Lynch, Tomas E. Ward, Alan F. Smeaton

The contribution is an assessment of commonly used quality metrics, and a comparison of their performances and the performance of human evaluations on an open dataset of T2V videos.

Predicting Injectable Medication Adherence via a Smart Sharps Bin and Machine Learning

no code implementations2 Apr 2020 Yingqi Gu, Akshay Zalkikar, Lara Kelly, Kieran Daly, Tomas E. Ward

Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment.

BIG-bench Machine Learning

A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation5 Mar 2020 Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy

In this work, we introduce an evaluation metric called Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.

Speech Synthesis

Synthesis of Realistic ECG using Generative Adversarial Networks

3 code implementations19 Sep 2019 Anne Marie Delaney, Eoin Brophy, Tomas E. Ward

Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack.

De-identification Inference Attack +3

Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy

3 code implementations4 Jun 2019 Zhengwei Wang, Qi She, Tomas E. Ward

While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.

Attribute Image Inpainting +4

A Neuro-AI Interface: Learning DNNs from the Human Brain

no code implementations28 May 2019 Zhengwei Wang, Qi She, Eoin Brophy, Alan F. Smeaton, Tomas E. Ward, Graham Healy

Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.

Object Recognition Open-Ended Question Answering

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

1 code implementation10 May 2019 Zhengwei Wang, Qi She, Alan F. Smeaton, Tomas E. Ward, Graham Healy

In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.

Image Generation Speech Synthesis

Quick and Easy Time Series Generation with Established Image-based GANs

no code implementations14 Feb 2019 Eoin Brophy, Zhengwei Wang, Tomas E. Ward

In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data.

Time Series Time Series Analysis +1

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

no code implementations15 Jan 2019 Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward

In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR).

EEG Electroencephalogram (EEG) +3

Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation

no code implementations10 Nov 2018 Zhengwei Wang, Graham Healy, Alan F. Smeaton, Tomas E. Ward

We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images.

Brain Computer Interface Generative Adversarial Network +1

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