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Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images.
Rabin, J., Peyré, G., Delon, J., Bernot, M.: Wasserstein barycenter and its application to texture mixing. In: International Conference on Scale Space and Variational Methods in Computer Vision (2011)
2011
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NeurIPS (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: ECCV (2016)
2016
Earlier work this paper cites.
Perarnau, G., Van De Weijer, J., Raducanu, B., Álvarez, J.M.: Invertible conditional gans for image editing. In: NeurIPS Workshop (2016)
2016
Earlier work this paper cites.
Zhu, J.Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: ECCV (2016)
2016
Earlier work this paper cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: ICML (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: ICLR (2017)
2017
Earlier work this paper cites.
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., Courville, A.: Adversarially learned inference. In: ICLR (2017)
2017
Earlier work this paper cites.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Cited alongside, same era.
Lipton, Z.C., Tripathi, S.: Precise recovery of latent vectors from generative adversarial networks. In: ICLR Workshop (2017)
2017
Cited alongside, same era.
Luo, J., Xu, Y., Tang, C., Lv, J.: Learning inverse mapping by autoencoder based generative adversarial nets. In: ICNIP (2017)
2017
Cited alongside, same era.
Creswell, A., Bharath, A.A.: Inverting the generator of a generative adversarial network. TNNLS (2018)
2018
Cited alongside, same era.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR (2018)
2018
Cited alongside, same era.
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: ICLR (2019)
2019
Later among the works it cites.
Goetschalckx, L., Andonian, A., Oliva, A., Isola, P.: Ganalyze: Toward visual definitions of cognitive image properties. In: ICCV (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)
2019
Later among the works it cites.
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Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1x1 convolutions. In: NeurIPS (2018)
2018
Cited alongside, same era.
Ma, F., Ayaz, U., Karaman, S.: Invertibility of convolutional generative networks from partial measurements. In: NeurIPS (2018)
2018
Cited alongside, same era.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: ICLR (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Bau, D., Strobelt, H., Peebles, W., Wulff, J., Zhou, B., Zhu, J.Y., Torralba, A.: Semantic photo manipulation with a generative image prior. SIGGRAPH (2019)
2019
Cited alongside, same era.
Bau, D., Zhu, J.Y., Wulff, J., Peebles, W., Strobelt, H., Zhou, B., Torralba, A.: Inverting layers of a large generator. In: ICLR Workshop (2019)
2019
Cited alongside, same era.
Bau, D., Zhu, J.Y., Wulff, J., Peebles, W., Strobelt, H., Zhou, B., Torralba, A.: Seeing what a gan cannot generate. In: ICCV (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Rameen, A., Yipeng, Q., Peter, W.: Image2stylegan: How to embed images into the stylegan latent space? In: ICCV (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: ICML (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Gu, J., Shen, Y., Zhou, B.: Image processing using multi-code gan prior. In: CVPR (2020)
2020
Closest in time.
2020
Closest in time.
Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of gans for semantic face editing. In: CVPR (2020)
2020
Closest in time.