Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan Willem van de Meent, and Robin Walters. Learning symmetric embeddings for equivariant world models. In International Conference on Machine Learning, 2022.
Luis A. Perez Rey, Vlado Menkovski, and Jim Portegies. Diffusion Variational Autoencoders. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2704-2710. International Joint Conferences on Artificial Intelligence Organization, July 2020. doi: 10.24963/ijcai.2020/375. URL https://doi.org/10.24963/ijcai.2020/375.
David Pfau, Irina Higgins, Alex Botev, and Sébastien Racanière. Disentangling by subspace diffusion. Advances in Neural Information Processing Systems, 33:17403-17415, 2020.
Robin Quessard, Thomas Barrett, and William Clements. Learning disentangled representations and group structure of dynamical environments. Advances in Neural Information Processing Systems, 33, 2020.
Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In International conference on machine learning, pages 1530-1538. PMLR, 2015.
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning, pages 1278-1286. PMLR, 2014.
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer. Normalizing flows on tori and spheres. In International Conference on Machine Learning, pages 8083-8092. PMLR, 2020.
Philip Shilane, Patrick Min, Michael Kazhdan, and Thomas Funkhouser. The princeton shape benchmark. In Proceedings Shape Modeling Applications, 2004., 2004.
Loek Tonnaer, Luis Armando Perez Rey, Vlado Menkovski, Mike Holenderski, and Jim Portegies. Quantifying and learning linear symmetry-based disentanglement. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 21584-21608. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/tonnaer22a.html.
Sharvaree Vadgama, Jakub Mikolaj Tomczak, and Erik J Bekkers. Kendall shape-vae: Learning shapes in a generative framework. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022.
Robin Winter, Marco Bertolini, Tuan Le, Frank Noe, and Djork-Arné Clevert. Unsupervised learning of group invariant and equivariant representations. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. URL https://openreview.net/forum?id=471pv23LDPr.
Jiacheng Xu and Greg Durrett. Spherical latent spaces for stable variational autoencoders. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4503-4513, 2018.
Tao Yang, Xuanchi Ren, Yuwang Wang, Wenjun Zeng, and Nanning Zheng. Towards building a group-based unsupervised representation disentanglement framework. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=YgPqNctmyd.
Xinqi Zhu, Chang Xu, and Dacheng Tao. Commutative lie group vae for disentanglement learning. In International Conference on Machine Learning, pages 12924-12934. PMLR, 2021.