BEGAN: Boundary Equilibrium Generative Adversarial Networks
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trade-off between image diversity and visual quality. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.
Code (18)
Tasks
DiversityImage GenerationSimilar Papers 제목 키워드 기반
Escaping from Collapsing Modes in a Constrained Space
Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training. We study the issue of mode collapse of Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of …
Generative Adversarial NetworkImage GenerationImage Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks
We propose a training and evaluation approach for autoencoder Generative Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative Adversarial Network (BEGAN), based on methods from the image quality …
Generative Adversarial NetworkImage Quality AssessmentA study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
Face frontalization is the process of synthesizing frontal facing views of faces given its angled poses. We implement a generative adversarial network (GAN) with spherical linear interpolation (Slerp) for frontalization …
Face GenerationGenerative Adversarial NetworkImage Quality Assessment Techniques Improve Training and Evaluation of Energy-Based Generative Adversarial Networks
We propose a new, multi-component energy function for energy-based Generative Adversarial Networks (GANs) based on methods from the image quality assessment literature. Our approach expands on the Boundary Equilibrium Ge…
Generative Adversarial NetworkImage Quality AssessmentSolving Inverse Problems with Conditional-GAN Prior via Fast Network-Projected Gradient Descent
The projected gradient descent (PGD) method has shown to be effective in recovering compressed signals described in a data-driven way by a generative model, i.e., a generator which has learned the data distribution. Furt…
compressed sensingGenerative Adversarial Network