[71] Karl Ridgeway and Michael C Mozer. Learning deep disentangled embeddings with the f-statistic loss. NeurIPS, 2018.
[72] Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas. Improving gans using optimal transport. *ICLR*, 2018.
[73] Victor Garcia Satorras, Emiel Hoogeboom, Fabian B Fuchs, Ingmar Posner, and Max Welling. E (n) equivariant normalizing flows. NeurIPS, 2021.
[74] Jürgen Schmidhuber. Learning factorial codes by predictability minimization. Neural computation, 1992.
[75] Uwe Schmidt and Stefan Roth. Learning rotation-aware features: From invariant priors to equivariant descriptors. In CVPR, 2012.
[76] Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, and Tarek Abdelzaher. Controlvae: Controllable variational autoencoder. In ICML. PMLR, 2020.
[77] Yujun Shen and Bolei Zhou. Closed-form factorization of latent semantics in gans. In CVPR, 2021.
[78] Yue Song, Nicu Sebe, and Wei Wang. Orthogonal svd covariance conditioning and latent disentanglement. IEEE T-PAMI, 2022.
[79] Yue Song, Andy Keller, Nicu Sebe, and Max Welling. Latent traversals in generative models as potential flows. In ICML. PMLR, 2023.
[80] Chang-Yu Tai, Ming-Yao Li, and Lun-Wei Ku. Hyperbolic disentangled representation for fine-grained aspect extraction. In AAAI, 2022.
[81] Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. Wasserstein auto-encoders. *ICLR*, 2018.
[82] Jakub Tomczak and Max Welling. Vae with a vampprior. In AISTATS. PMLR, 2018.
[83] Alexander Tong, Jessie Huang, Guy Wolf, David Van Dijk, and Smita Krishnaswamy. Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics. In ICML. PMLR, 2020.
[84] Christos Tzelepis, Georgios Tzimiropoulos, and Ioannis Patras. WarpedGANSpace: Finding non-linear rbf paths in GAN latent space. In ICCV, 2021.
[85] Elise Van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, and Max Welling. Mdp homomorphic networks: Group symmetries in reinforcement learning. NeurIPS, 2020.
[86] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017.
[87] Cédric Villani. Optimal transport: old and new, volume 338. Springer, 2009.
[88] Cédric Villani. Topics in optimal transportation, volume 58. American Mathematical Soc., 2021.
[89] Andrey Voynov and Artem Babenko. Unsupervised discovery of interpretable directions in the gan latent space. In ICML, 2020.
[90] Yuxiang Wei, Yupeng Shi, Xiao Liu, Zhilong Ji, Yuan Gao, Zhongqin Wu, and Wangmeng Zuo. Orthogonal jacobian regularization for unsupervised disentanglement in image generation. In ICCV, 2021.
[91] James CR Whittington, Will Dorrell, Surya Ganguli, and Timothy Behrens. Disentanglement with biological constraints: A theory of functional cell types. In ICLR, 2023.
[92] Daniel Worrall and Max Welling. Deep scale-spaces: Equivariance over scale. NeurIPS, 2019.
[93] Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow. Harmonic networks: Deep translation and rotation equivariance. In CVPR, 2017.
[94] Liu Yang and George Em Karniadakis. Potential flow generator with 12 optimal transport regularity for generative models. IEEE TNNLS, 2020.
[95] Tao Yang, Yuwang Wang, Yan Lv, and Nanning Zh. Disdiff: Unsupervised disentanglement of diffusion probabilistic models. arXiv preprint arXiv:2301.13721, 2023.
[96] Cagatay Yildiz, Markus Heinonen, and Harri Lahdesmaki. Ode2vae: Deep generative second order odes with bayesian neural networks. NeurIPS, 2019.