Fetching the paper…
Reading the bibliography…
Significant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. In Advances in Neural Information Processing Systems (2014)
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: Quantifying interpretability of deep visual representations. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6541–6549 (2017)
2017
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 633–641 (2017)
2017
Earlier work this paper cites.
Bau, D., Zhu, J.Y., Strobelt, H., Zhou, B., Tenenbaum, J.B., Freeman, W.T., Torralba, A.: Gan dissection: Visualizing and understanding generative adversarial networks. International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Goetschalckx, L., Andonian, A., Oliva, A., Isola, P.: Ganalyze: Toward visual definitions of cognitive image properties. In: Proc. International Conference on Computer Vision (ICCV). pp. 5744–5753 (2019)
2019
Earlier work this paper cites.
Jahanian, A., Chai, L., Isola, P.: On the ”steerability” of generative adversarial networks. International Conference on Learning Representations (2019)
2019
Earlier work this paper cites.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4401–4410 (2019)
2019
Cited alongside, same era.
Härkönen, E., Hertzmann, A., Lehtinen, J., Paris, S.: Ganspace: Discovering interpretable gan controls. In: In Advances in Neural Information Processing Systems (2020)
2020
Cited alongside, same era.
Peebles, W., Peebles, J., Zhu, J.Y., Efros, A., Torralba, A.: The hessian penalty: A weak prior for unsupervised disentanglement. Proc. European Conference on Computer Vision (ECCV) (2020)
2020
Cited alongside, same era.
Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of gans for semantic face editing. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9243–9252 (2020)
2020
Cited alongside, same era.
He, Z., Kan, M., Shan, S.: Eigengan: Layer-wise eigen-learning for gans. Proc. International Conference on Computer Vision (ICCV) (2021)
2021
Closest in time.
Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., Aila, T.: Alias-free generative adversarial networks. In Advances in Neural Information Processing Systems (2021)
2021
Closest in time.
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: Text-driven manipulation of stylegan imagery. Proc. International Conference on Computer Vision (ICCV) (2021)
2021
Closest in time.
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shen, Y., Yang, C., Tang, X., Zhou, B.: Interfacegan: Interpreting the disentangled face representation learned by gans. IEEE Trans. on Pattern Analysis and Machine Intelligence (2020)
2020
Cited alongside, same era.
Voynov, A., Babenko, A.: Unsupervised discovery of interpretable directions in the gan latent space. In: International Conference on Machine Learning (2020)
2020
Cited alongside, same era.
Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG) 40
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Shen, Y., Zhou, B.: Closed-form factorization of latent semantics in gans. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1532–1540 (2021)
2021
Closest in time.
Yang, C., Shen, Y., Zhou, B.: Semantic hierarchy emerges in deep generative representations for scene synthesis. International Journal of Computer Vision 129
2021
Closest in time.
Zhang, Y., Chen, W., Ling, H., Gao, J., Zhang, Y., Torralba, A., Fidler, S.: Image gans meet differentiable rendering for inverse graphics and interpretable 3d neural rendering. International Conference on Learning Representations (2021)
2021
Closest in time.
Xia, W., Zhang, Y., Yang, Y., Xue, J.H., Zhou, B., Yang, M.H.: Gan inversion: A survey. IEEE Trans. on Pattern Analysis and Machine Intelligence (2022)
2022
Closest in time.