How Generative Adversarial Networks and Their Variants Work

Hong, Yongjun; Hwang, Uiwon; Yoo, Jaeyoon; Yoon, Sungroh

doi:10.1145/3301282

Cited by 263 publications

(158 citation statements)

References 132 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…The last couple of years have seen impressive results for photo-realistic image synthesis using deep learning techniques, especially generative adversarial networks (GANs, introduced by Goodfellow et al in 2014 [80]), e.g. [205,206,207]. These can also be used for biological image synthesis [208,209] and text-to-image synthesis [210,211,212].…”

Section: Image Synthesismentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,514

883

View full text Add to dashboard Cite

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

show abstract

Section: Image Synthesismentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,514

883

View full text Add to dashboard Cite

show abstract

“…Therefore, in fact, they share the same hidden layer of a single network except for their input and output layers. In addition, for fair comparison with previous works, we adopted DCGAN [ 42 ] to construct both our multi-agent generator

and our discriminator D networks, allowing us to use the same network architecture and training procedure. DCGAN is a widely adopted modeling architecture that has been used in various GAN variants due to its stability during adversarial training using convolutional neural networks.…”

Section: Methodsmentioning

confidence: 99%

Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation

Zhao

Xiao

et al. 2020

Entropy

View full text Add to dashboard Cite

Generative adversarial networks (GANs), which are a promising type of deep generative network, have recently drawn considerable attention and made impressive progress. However, GAN models suffer from the well-known problem of mode collapse. This study focuses on this challenge and introduces a new model design, called the encoded multi-agent generative adversarial network (E-MGAN), which tackles the mode collapse problem by introducing the variational latent representations learned from a variable auto-encoder (VAE) to a multi-agent GAN. The variational latent representations are extracted from training data to replace the random noise input of the general multi-agent GANs. The generator in E-MGAN employs multiple generators and is penalized by a classifier. This integration guarantees that the proposed model not only enhances the quality of generated samples but also improves the diversity of generated samples to avoid the mode collapse problem. Moreover, extensive experiments are conducted on both a synthetic dataset and two large-scale real-world datasets. The generated samples are visualized for qualitative evaluation. The inception score (IS) and Fréchet inception distance (FID) are adopted to measure the performance of the model for quantitative assessment. The results confirmed that the proposed model achieves outstanding performances compared to other state-of-the-art GAN variants.

show abstract

“…Due to the GANs' excellent performance in generation of realistic-looking images, a number of articles track the recent advancements of GANs (27)(28)(29). The main reasons behind this success are the inherent advantage of being an unsupervised training method to obtain pieces of information over data (30), as well as the significant performance in the extraction of visual features by discovering the high dimensional underlying distribution of the data.…”

Section: Generative Adversarial Neural Networkmentioning

confidence: 99%

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need

Arafati

Hu²,

Finn³

et al. 2019

Cardiovasc. Diagn. Ther.

View full text Add to dashboard Cite

Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies.

show abstract

How Generative Adversarial Networks and Their Variants Work

Cited by 263 publications

References 132 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need

Contact Info

Product

Resources

About