Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments

• URL: http://arxiv.org/abs/2005.13178v1
• Date: Wed, 27 May 2020 05:56:53 GMT
• Title: Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments
• Authors: Pegah Salehi, Abdolah Chalechale, Maryam Taghizadeh
• Abstract summary: Generative Adversarial Network (GAN) is an effective method to produce samples of large-scale data distribution. GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously.
• Score: 9.023847175654602
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined.

Related papers

• Generative adversarial networks for data-scarce spectral applications [0.0]
  We report on an application of GANs in the domain of synthetic spectral data generation. We show that CWGANs can act as a surrogate model with improved performance in the low-data regime.
  arXiv  Detail & Related papers  (2023-07-14T16:27:24Z)
• Generative Adversarial Reduced Order Modelling [0.0]
  We present GAROM, a new approach for reduced order modelling (ROM) based on generative adversarial networks (GANs) GANs have the potential to learn data distribution and generate more realistic data. In this work, we combine the GAN and ROM framework, by introducing a data-driven generative adversarial model able to learn solutions to parametric differential equations.
  arXiv  Detail & Related papers  (2023-05-25T09:23:33Z)
• On-Device Domain Generalization [93.79736882489982]
  Domain generalization is critical to on-device machine learning applications. We find that knowledge distillation is a strong candidate for solving the problem. We propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data.
  arXiv  Detail & Related papers  (2022-09-15T17:59:31Z)
• Data-Free Adversarial Knowledge Distillation for Graph Neural Networks [62.71646916191515]
  We propose the first end-to-end framework for data-free adversarial knowledge distillation on graph structured data (DFAD-GNN) To be specific, our DFAD-GNN employs a generative adversarial network, which mainly consists of three components: a pre-trained teacher model and a student model are regarded as two discriminators, and a generator is utilized for deriving training graphs to distill knowledge from the teacher model into the student model. Our DFAD-GNN significantly surpasses state-of-the-art data-free baselines in the graph classification task.
  arXiv  Detail & Related papers  (2022-05-08T08:19:40Z)
• Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation [5.196831100533835]
  Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field. GANs are typically used to generate or transform synthetic images. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks.
  arXiv  Detail & Related papers  (2021-05-10T08:28:36Z)
• On the Fairness of Generative Adversarial Networks (GANs) [1.061960673667643]
  Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. In this paper, we analyze and highlight fairness concerns of GANs model.
  arXiv  Detail & Related papers  (2021-03-01T12:25:01Z)
• Improving Generative Adversarial Networks with Local Coordinate Coding [150.24880482480455]
  Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution. In practice, semantic information might be represented by some latent distribution learned from data. We propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
  arXiv  Detail & Related papers  (2020-07-28T09:17:50Z)
• Lessons Learned from the Training of GANs on Artificial Datasets [0.0]
  Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. GANs are prone to underfitting or overfitting, making the analysis of them difficult and constrained. We train them on artificial datasets where there are infinitely many samples and the real data distributions are simple. We find that training mixtures of GANs leads to more performance gain compared to increasing the network depth or width.
  arXiv  Detail & Related papers  (2020-07-13T14:51:02Z)
• Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator [85.68825725223873]
  Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. We introduce the Discriminator Contrastive Divergence, which is well motivated by the property of WGAN's discriminator. We demonstrate the benefits of significant improved generation on both synthetic data and several real-world image generation benchmarks.
  arXiv  Detail & Related papers  (2020-04-05T01:50:16Z)
• GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences [70.30467057209405]
  Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs are designed in a model-free fashion where no additional information about the underlying distribution is available. We propose a principled design of a model-based GAN that uses a set of simple discriminators on the neighborhoods of the Bayes-net/MRF.
  arXiv  Detail & Related papers  (2020-03-02T04:31:22Z)
• Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets [70.62568022925971]
  generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. In many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In this paper, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
  arXiv  Detail & Related papers  (2020-02-02T02:58:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.