• Corpus ID: 10851832

Comparative Study on Generative Adversarial Networks

@article{Hitawala2018ComparativeSO,
  title={Comparative Study on Generative Adversarial Networks},
  author={Saifuddin Hitawala},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.04271}
}
In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been… 
View PDF on arXiv
33 Citations
Highly Influential Citations
2
Background Citations
21
Methods Citations
5

Figures and Tables from this paper

33 Citations

Comparison on Generative Adversarial Networks –A Study
TLDR
An overview of GANs along with its comparison with other networks, as well as different versions of Generative Adversarial Networks are provided.
A Detailed Study on Generative Adversarial Networks
  • Shailender KumarS. Dhawan
  • Computer Science
    2020 5th International Conference on Communication and Electronics Systems (ICCES)
  • 2020
TLDR
This paper will give an overview of GAN and discuss the popular variants of this model, its common applications, different evaluation metrics proposed for it, and finally its drawbacks, conclusion of the paper and future course of action.
Stabilizing Generative Adversarial Networks: A Survey
TLDR
A comprehensive overview of the GAN training stabilization methods is provided, which discusses the advantages and disadvantages of each approach, offers a comparative summary, and concludes with a discussion of open problems.
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy.
TLDR
The objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements.
Generative Adversarial Networks: A Survey and Taxonomy
TLDR
An in-depth review of GAN-related research in the literature is provided and an account of the architecture-variants and loss-variant GANs, which have been proposed to handle these three challenges from two perspectives are provided.
Generative Adversarial Networks : A Survey
TLDR
A comprehensive review of the original GAN model and its modified versions is provided, covering broad topics on model architecture, Objective and Loss Functions, and applications, and the technical challenges and several promising directions are highlighted.
An analysis of generative adversarial networks and variants for image synthesis on MNIST dataset
TLDR
An analysis of working and architecture of GAN and its popular variants for image generation in detail is conducted and insights regarding existing limitations and challenges faced by GAN are given.
Stabilizing Generative Adversarial Network Training: A Survey
TLDR
This survey summarizes the approaches and methods employed for the purpose of stabilizing GAN training procedure and discusses the advantages and disadvantages of each of the methods, offering a comparative summary of the literature on stabilizing gan training procedure.
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
TLDR
This work surveys several training solutions proposed by different researchers to stabilize GAN training, and surveys the original GAN model and its modified classical versions, and detail analysis of various GAN applications in different domains.
Generative Adversarial Networks ( GANs ) : The Progress So Far In Image Generation
TLDR
This summary paper compares and contrast different results and put forth a summary of theoretical contributions about GANs with focus on image/visual applications, to give a bird’s eye view to a person wishing to understand the theoretical progress in GAns so far.
...
...

References

SHOWING 1-10 OF 12 REFERENCES
Generating images with recurrent adversarial networks
TLDR
This work proposes a recurrent generative model that can be trained using adversarial training to generate very good image samples, and proposes a way to quantitatively compare adversarial networks by having the generators and discriminators of these networks compete against each other.
Conditional Generative Adversarial Nets
TLDR
The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels.
Boundary-Seeking Generative Adversarial Networks
TLDR
This work introduces a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
  • Guo-Jun Qi
  • Computer Science
    International Journal of Computer Vision
  • 2019
TLDR
The Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN) are presented, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN.
Adversarial Feature Learning
TLDR
Bidirectional Generative Adversarial Networks are proposed as a means of learning the inverse mapping of GANs, and it is demonstrated that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TLDR
This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
SEGAN: Speech Enhancement Generative Adversarial Network
TLDR
This work proposes the use of generative adversarial networks for speech enhancement, and operates at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
TLDR
Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
TLDR
A generative parametric model capable of producing high quality samples of natural images using a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion.
Generative Adversarial Nets
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a…
...
...