Generative Adversarial Network
912 papers with code • 0 benchmarks • 0 datasets
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StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
coqui-ai/TTS
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We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.
The relativistic discriminator: a key element missing from standard GAN
AlexiaJM/RelativisticGAN
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ICLR 2019
We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
Modeling Tabular data using Conditional GAN
DAI-Lab/CTGAN
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NeurIPS 2019
Tabular data usually contains a mix of discrete and continuous columns.
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
layumi/Person-reID_GAN
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ICCV 2017
We verify the proposed method on a practical problem: person re-identification (re-ID).
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Image De-raining Using a Conditional Generative Adversarial Network
nekitmm/starnet
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Hence, it is important to solve the problem of single image de-raining/de-snowing.
