Generative Adversarial Network
908 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Generative Adversarial Network models and implementationsMost implemented papers
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
tensorflow/models
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CVPR 2017
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Self-Attention Generative Adversarial Networks
brain-research/self-attention-gan
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arXiv 2018
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
xinntao/ESRGAN
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To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
eriklindernoren/PyTorch-GAN
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NeurIPS 2016
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.
Autoencoding beyond pixels using a learned similarity metric
clementchadebec/benchmark_VAE
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We present an autoencoder that leverages learned representations to better measure similarities in data space.
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
yxgeee/MMT
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CVPR 2018
Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e. g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network.
SEGAN: Speech Enhancement Generative Adversarial Network
santi-pdp/segan
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In contrast to current techniques, we operate 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.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
taoxugit/AttnGAN
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CVPR 2018
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
LeeDoYup/AnoGAN
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Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
