Generative adversarial network ≈ Generative adversarial network
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Generative adversarial networks Open
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distr…
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Spectral Normalization for Generative Adversarial Networks Open
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discr…
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Conditional Image Synthesis With Auxiliary Classifier GANs Open
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We c…
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Spectral Normalization for Generative Adversarial Networks Open
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discr…
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Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss Open
The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifac…
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NIPS 2016 Tutorial: Generative Adversarial Networks Open
This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs…
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Open
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that…
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Generative Adversarial Text to Image Synthesis Open
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been develope…
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks Open
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant ar…
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Training Generative Adversarial Networks with Limited Data Open
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes train…
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Energy-based Generative Adversarial Network Open
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Simil…
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Alias-Free Generative Adversarial Networks Open
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearin…
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A Style-Based Generator Architecture for Generative Adversarial Networks Open
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g.,…
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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks Open
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given de…
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Recent Progress on Generative Adversarial Networks (GANs): A Survey Open
Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent pro…
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Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using\n Generative Models Open
In recent years, deep neural network approaches have been widely adopted for\nmachine learning tasks, including classification. However, they were shown to\nbe vulnerable to adversarial perturbations: carefully crafted small\nperturbations…
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Semi-Supervised Learning with Generative Adversarial Networks Open
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N…
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Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network Open
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart’s activity. However, automated medical-aided diagnosis with computers usually requires a larg…
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GAIN: Missing Data Imputation using Generative Adversarial Nets Open
We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some comp…
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Face aging with conditional generative adversarial networks Open
It has been recently shown that Generative Adversarial Networks (GANs) can\nproduce synthetic images of exceptional visual fidelity. In this work, we\npropose the GAN-based method for automatic face aging. Contrary to previous\nworks emplo…
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Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models Open
In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a method that c…
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Data synthesis based on generative adversarial networks Open
Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi…
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A de novo molecular generation method using latent vector based generative adversarial network Open
Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network f…
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C-RNN-GAN: Continuous recurrent neural networks with adversarial training Open
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a colle…
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Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN Open
Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms. Malware authors usually have no access to the detailed structures and parameters of the machine l…
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GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks Open
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on…
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Generating Multi-label Discrete Patient Records using Generative Adversarial Networks Open
Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data…
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SalGAN: Visual Saliency Prediction with Generative Adversarial Networks Open
We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed …
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Inverting the Generator of a Generative Adversarial Network Open
Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, throu…
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Inverse design of porous materials using artificial neural networks Open
We have developed a user-desired generative adversarial network and used them to generate 121 zeolite materials.