Abstract:
Generative Adversarial Networks (GAN) have attained remarkable results in image
augmentation and generation. GAN models consist of Latent space vectors. Latent space
vectors are interpreted as inputs for the generator to generate images. Mathematically they are
a hypersphere consisting of many dimensions, containing the learnt representation. However,
various GAN variants use different techniques to model latent space vectors. This dissertation
dissects the latent space vectors of state-of-the-arts GAN variants, for image synthesis. We
also compare and contrast optimization methods of latent space vectors in existing literature.
In this dissertation, we undertake a comparative analysis of latent space vectors and
optimization methods of the popular image synthesizing GAN models.
Based on the analysis we were able to choose appropriate existing works to carry out the
research. We choose the DCGAN as the model and ClusterGAN as the model optimization
technique. Based on a review of mathematical methods we choose manifold learning
techniques to cluster the latent space. The results of the clustering allowed us to determine the
sparse and dense features of a Euclidean image distribution.
The research on the image augmentation techniques allowed the authors to make a
deployable facial recognition system utilizing advanced image models. While there are other
facial recognition models, they are not deployable in a business environment, they are
procedural in nature. The authors had to review existing work on face recognition such as
FaceNet model to make the system. The authors also used concurrent programming
principles to optimize the model to train in reasonable amounts of time.
This will be useful with pandemics such as the COVID-19 as well. We need more robustness
in non-invasive (touchless) authentication systems. Most authentication systems such as
fingerprint recognition are touch-based, this is a risk during pandemics.
