Least Squares Generative Adversarial Networks (original) (raw)

On the Effectiveness of Least Squares Generative Adversarial Networks

IEEE transactions on pattern analysis and machine intelligence, 2018

Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson x2x^{2}x2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We train LSGANs on several datasets, and the experimental results show that the images generated by LSGANs are of better quality than regular GANs. Furthermore, we evaluate the stability of LSGANs in two ...

Review on Generative Adversarial Networks

In the last few years, a type of generative model known as Generative Adversarial Networks (GANs), has achieved tremendous success mainly in the field of computer vision, image classification, speech and language processing, etc. GANs are the models which are used to produce new samples which have similar data distribution as of the training dataset. In this review paper, we will first introduce the idea behind the GANs, followed by a brief overview of various types of GANs as well as comparing it with different generative models. Then, we will discuss the application range and finally the future work with its associated research frontiers.

Advances of Generative Adversarial Networks: A Survey

2020

Generative Adversarial Networks (GANs) are part of the deep generative model family and able to generate synthetic samples based on the underlying distribution of real-world data. With expanding interest new discoveries and recent advances are hard to follow. Recent advancements to stabilize training, will help GANs to open up new domains using adjusted architectures and loss functions. Various findings show, that GANS can be used to generate not only images, but is also useful for text and audio creation. This paper, presents an overview of different GAN architectures, giving summaries of the underlying fundamentals of each presented GAN. Furthermore, this paper presents look into four application domains and lists additional domains. Additionally, this paper summaries datasets and metrics used to evaluate GANs and present recent scientific advancements. Keywords–generative adversarial networks; machine learning; deep learning.

Linear Discriminant Generative Adversarial Networks

arXiv (Cornell University), 2017

We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN). The discriminator of an LD-GAN is trained to maximize the linear separability between distributions of hidden representations of generated and targeted samples, while the generator is updated based on the decision hyper-planes computed by performing LDA over the hidden representations. LD-GAN provides a concrete metric of separation capacity for the discriminator, and we experimentally show that it is possible to stabilize the training of LD-GAN simply by calibrating the update frequencies between generators and discriminators in the unsupervised case, without employment of normalization methods and constraints on weights. In the class conditional generation tasks, the proposed method shows improved training stability together with better generalization performance compared to WGAN (Arjovsky et al. 2017) that employs an auxiliary classifier.

Survey on generative adversarial networks

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY

GAN stands for Generative Adversarial Networks. GANs are the most interesting topics in Deep Learning. The concept of GAN is introduced by Ian Good Fellow and his colleagues at the University of Montreal. The main architecture of GAN contains two parts: one is a Generator and the other is Discriminator. The name Adversarial stands for conflict and here the conflict is present between Generator and Discriminator. And hence the name adversarial comes to this concept. In this paper, the author has investigated different ways GAN's are used in real time applications and what are the different types of GAN's present. GAN's are mainly important for generating new data from existing ones. As a machine learning model cannot work properly if the size of the dataset is small GAN's are here to help to increase the size by creating new fake things from original ones. GAN's are also used in creating images from the given words that are text-to-image conversion. GANs are also applied in image resolution, image translation and in many other scenarios. From this survey on GAN author aim to know what are the different applications of GAN that are present and their scope. The author has also aimed at knowing the different types of GAN's available at present.

Generative Adversarial Networks:Introduction and Outlook

Recently, generative adversarial networks (GANs) have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

Generative Adversarial Networks and Other Generative Models

arXiv (Cornell University), 2022

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be highly successful-though by no means from the first attempt. This chapter gives a basic introduction into the motivation for Generative Adversarial Networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism, and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, and also typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.

Generative adversarial networks: a survey on applications and challenges

International Journal of Multimedia Information Retrieval, 2020

Deep neural networks have attained great success in handling high dimensional data, especially images. However, generating naturalistic images containing ginormous subjects for different tasks like image classification, segmentation, object detection, reconstruction, etc., is continued to be a difficult task. Generative modelling has the potential to learn any kind of data distribution in an unsupervised manner. Variational autoencoder (VAE), autoregressive models, and generative adversarial network (GAN) are the popular generative modelling approaches that generate data distributions. Among these, GANs have gained much attention from the research community in recent years in terms of generating quality images and data augmentation. In this context, we collected research articles that employed GANs for solving various tasks from popular databases and summarized them based on their application. The main objective of this article is to present the nuts and bolts of GANs, state-of-the-art related work and its applications, evaluation metrics, challenges involved in training GANs, and benchmark datasets that would benefit naive and enthusiastic researchers who are interested in working on GANs.

A Critical Review of Generative Adversarial Networks based on Stability Criteria

In the machine learning field, the traditional deep learning models are mostly of discriminative type in which their goal is to discover a map from input layers to output layers. Also, these models require large amount of annotated data for training. On the other hand, deep generative models (DGMs) provide a new way to learn features effectively from the sample data which do not require the labeled data. Among the many DGMs, generative adversarial networks (GANs) are the emerging models for both semi-supervised and unsupervised learning. GANs use a pair of discriminator and generator networks which are used in competitive process to learn the effective features. However, the implementation of GANs suffers against the challenging problem of stability of training. This paper discusses the review and challenges of the implementation of GANs. We review different GAN models such as deep convolutional GAN (DCGAN), Wasserstein GAN (WGAN), WGAN with gradient penalty (WGAN-GP) and boundary e...

Exploring The Potential of Generative Adversarial Network: A Comparative Study Of GAN

IRJET, 2023

Generative Adversarial Networks (GANs), a class of deep learning models that creates new data samples that resemble the original data, are in-depth examined in this research study. The article covers many GAN subtypes, including vanilla GANs, MedGANs, StyleGANs, and CycleGANs, and analyses their designs and training approaches. The study examines the many GAN applications, including text-to-image synthesis, data augmentation, and picture and video creation. There is also discussion of the difficulties each type of GAN method faces, including mode collapse, instability, and vanishing gradients. In-depth analysis is also given to the technical features of GANs, including the generator and discriminator networks, training loss functions, and regularization techniques. The research study examines current advancements in GANs, including self-attention, adversarial autoencoders, and attention mechanisms. Additionally, the paper addresses the ethical issues related to GANs, such as the possible exploitation of data created by GANs and bias in training data. The future potential and developments of GANs are discussed in the study, including its use to unsupervised representation learning and the creation of novel GAN architectures. The study emphasizes the need for more study to overcome GANs' problems and broaden their application to other fields. GANs are a fast-developing subject of study with enormous potential in many areas.