1 citations · 1 across the 3 of their papers we have counts for
3 papers
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks
Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Datasets are crucial when training a deep neural network. When datasets are unrepresentative, trained models are prone to bias because they are unable to generalise to real world s…
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms
Mkhuseli Ngxande, Jule-Raymond Tapamo, Michael Burke
Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follo…
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis
Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Recent work has shown significant progress in the direction of synthetic data generation using Generative Adversarial Networks (GANs). GANs have been applied in many fields of comp…