259 citations · 313 across the 5 of their papers we have counts for
7 papers
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
Muhammad Ali Farooq, Wang Yao, Michael Schukat +2
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustness of machine learning model t…
A lightweight 3D dense facial landmark estimation model from position map data
Shubhajit Basak, Sathish Mangapuram, Gabriel Costache +2
The incorporation of 3D data in facial analysis tasks has gained popularity in recent years. Though it provides a more accurate and detailed representation of the human face, accru…
Methodology for Building Synthetic Datasets with Virtual Humans
Shubhajit Basak, Hossein Javidnia, Faisal Khan +2
Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation pro…
High-Accuracy Facial Depth Models derived from 3D Synthetic Data
Faisal Khan, Shubhajit Basak, Hossein Javidnia +2
In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neur…
Deep Reinforcement Learning: An Overview
Seyed Sajad Mousavi, Michael Schukat, Enda Howley
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern re…
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Seyed Sajad Mousavi, Michael Schukat, Enda Howley
Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems wit…
Learning to predict where to look in interactive environments using deep recurrent q-learning
Sajad Mousavi, Michael Schukat, Enda Howley +2
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games…