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The impressive success of style-based GANs (StyleGANs) in high-fidelity image synthesis has motivated research to understand the semantic properties of their latent spaces.
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Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2006
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Determining the number of components in a factor model from limited noisy data
Kritchman, S. and Nadler, B · 2008
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Candès, E. J., Li, X., Ma, Y., and Wright, J · 2011
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Bengio, Y., Courville, A., and Vincent, P · 2013
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Minimax risk of matrix denoising by singular value thresholding
Donoho, D. and Gavish, M · 2014
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Schubert varieties and distances between subspaces of different dimensions
Ye, K. and Lim, L.-H · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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An introduction to grassmann manifolds and their matrix representation
Karrasch, D · 2017
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2019
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Ganspace: Discovering interpretable gan controls
Härkönen, E., Hertzmann, A., Lehtinen, J., and Paris, S · 2020
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Controlling generative models with continuous factors of variations
Plumerault, A., Borgne, H. L., and Hudelot, C · 2020
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Interpreting the latent space of gans for semantic face editing
Shen, Y., Gu, J., Tang, X., and Zhou, B · 2020
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Unsupervised discovery of interpretable directions in the gan latent space
Voynov, A. and Babenko, A · 2020
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Stylespace analysis: Disentangled controls for stylegan image generation
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Brock, A., Donahue, J., and Simonyan, K · 2018
Cited alongside, same era.
A framework for the quantitative evaluation of disentangled representations
Eastwood, C. and Williams, C. K · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Cited alongside, same era.
A spectral regularizer for unsupervised disentanglement
Ramesh, A., Choi, Y., and LeCun, Y · 2018
Cited alongside, same era.
Image2stylegan: How to embed images into the stylegan latent space?
Abdal, R., Qin, Y., and Wonka, P · 2019
Cited alongside, same era.
Ganalyze: Toward visual definitions of cognitive image properties
Goetschalckx, L., Andonian, A., Oliva, A., and Isola, P · 2019
Cited alongside, same era.
On the” steerability” of generative adversarial networks
Jahanian, A., Chai, L., and Isola, P · 2019
Cited alongside, same era.
Wu, Z., Lischinski, D., and Shechtman, E · 2020
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Improved stylegan embedding: Where are the good latents?
Zhu, P., Abdal, R., Qin, Y., Femiani, J., and Wonka, P · 2020
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Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows
Abdal, R., Zhu, P., Mitra, N. J., and Wonka, P · 2021
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Alias-free generative adversarial networks
Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., and Aila, T · 2021
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Styleclip: Text-driven manipulation of stylegan imagery
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., and Lischinski, D · 2021
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Closed-form factorization of latent semantics in gans
Shen, Y. and Zhou, B · 2021
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Zhu, J., Feng, R., Shen, Y., Zhao, D., Zha, Z., Zhou, J., and Chen, Q · 2021
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Stylegan-xl: Scaling stylegan to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
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