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In cases where a Wasserstein GAN depends on a condition the latter is usually handled via an expectation within the loss function.
Conditional generative adversarial nets
Mirza, M., and Osindero, S · 2014
Earlier work this paper cites.
Wasserstein GAN
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
Improved training of wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Earlier work this paper cites.
Banach wasserstein GAN
Adler, J., and Lunz, S · 2018
Earlier work this paper cites.
Adler, J., and Öktem, O · 2018
Cited alongside, same era.
Eeg data augmentation for emotion recognition using a conditional wasserstein GAN
Luo, Y., and Lu, B.-L · 2018
Cited alongside, same era.
Deep posterior sampling: Uncertainty quantification for large scale inverse problems
Adler, J., and Öktem, O · 2019
Cited alongside, same era.
Single image haze removal using conditional wasserstein generative adversarial networks
Ebenezer, J. P., Das, B., and Mukhopadhyay, S · 2019
Cited alongside, same era.
Image generation method based on improved condition GAN
Jin, Q., Luo, X., Shi, Y., and Kita, K · 2019
Later among the works it cites.
Conditional WGAN for grasp generation
Patzelt, F., Haschke, R., and Ritter, H. J · 2019
Later among the works it cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Later among the works it cites.
High perceptual quality image denoising with a posterior sampling cgan
Ohayon, G., Adrai, T., Vaksman, G., Elad, M., and Milanfar, P · 2021
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