36 research outputs found
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional method which we call Smart Augmentation and we show how to use it to
increase the accuracy and reduce overfitting on a target network. Smart
Augmentation works by creating a network that learns how to generate augmented
data during the training process of a target network in a way that reduces that
networks loss. This allows us to learn augmentations that minimize the error of
that network.
Smart Augmentation has shown the potential to increase accuracy by
demonstrably significant measures on all datasets tested. In addition, it has
shown potential to achieve similar or improved performance levels with
significantly smaller network sizes in a number of tested cases
September 30, 1999
The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia
Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets
StyleGAN is a state-of-art generative adversarial network architecture that
generates random 2D high-quality synthetic facial data samples. In this paper,
we recap the StyleGAN architecture and training methodology and present our
experiences of retraining it on a number of alternative public datasets.
Practical issues and challenges arising from the retraining process are
discussed. Tests and validation results are presented and a comparative
analysis of several different re-trained StyleGAN weightings is provided 1. The
role of this tool in building large, scalable datasets of synthetic facial data
is also discussed