no code implementations • 10 Jan 2024 • Muhammad Ali Farooq, Wang Yao, Michael Schukat, Mark A Little, Peter Corcoran
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 training.
1 code implementation • 29 Aug 2023 • Shubhajit Basak, Sathish Mangapuram, Gabriel Costache, Rachel McDonnell, Michael Schukat
As there is no public dataset available containing dense landmarks, we propose a pipeline to create a dense keypoint training dataset containing 520 key points across the whole face from an existing facial position map data.
no code implementations • 21 Jun 2020 • Shubhajit Basak, Hossein Javidnia, Faisal Khan, Rachel McDonnell, Michael Schukat
Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset.
no code implementations • 23 Jun 2018 • 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 recognition, speech recognition, computer vision, and natural language processing.
no code implementations • 28 Apr 2017 • 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 with high dimensional state and action spaces.
no code implementations • 17 Dec 2016 • Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, Nasser Mozayani
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).