[15] Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Scholkopf, Ole Winther, and Francesco Locatello. Generalization and robustness implications in object-centric learning. arXiv preprint arXiv:2107.00637, 2021.
[16] Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch. Unsupervised learning of compositional energy concepts. Advances in Neural Information Processing Systems, 34, 2021.
[17] Yilun Du, Kevin Smith, Tomer Ulman, Joshua Tenenbaum, and Jiajun Wu. Unsupervised discovery of 3d physical objects from video. arXiv preprint arXiv:2007.12348, 2020.
[18] Martin Engelcke, Oiwi Parker Jones, and Ingmar Posner. GENESIS-V2: Inferring unordered object representations without iterative refinement. arXiv preprint arXiv:2104.09958, 2021.
[19] Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner. Genesis: Generative scene inference and sampling with object-centric latent representations. In International Conference on Learning Representations, 2020.
[20] SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, and Geoffrey E Hinton. Attend, infer, repeat: Fast scene understanding with generative models. In Advances in Neural Information Processing Systems, pages 3225-3233, 2016.
[21] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021.
[22] Jerry A Fodor and Zenon W Pylyshyn. Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2):3-71, 1988.
[23] Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, and Sjoerd van Steenkiste. Unsupervised learning of temporal abstractions with slot-based transformers. arXiv preprint arXiv:2203.13573, 2022.
[24] Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam H. Laradji, Hsueh-Ti Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, and Andrea Tagliausacchi. Kubric: A scalable dataset generator. arXiv preprint arXiv:2203.03570, 2022.
[25] Klaus Greff, Raphaël Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthew, Matthew Botvinick, and Alexander Lerchner. Multi-object representation learning with iterative variational inference. arXiv preprint arXiv:1903.00450, 2019.
[26] Klaus Greff, Sjoerd van Steenkiste, and Jurgen Schmidhuber. Neural expectation maximization. In Advances in Neural Information Processing Systems, pages 6691-6701, 2017.
[27] Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber. On the binding problem in artificial neural networks. arXiv preprint arXiv:2012.05208, 2020.
[28] Olivier J Henaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, and Relja Arandjelović. Object discovery and representation networks. In ECCV, pages 123–143. Springer, 2022.
[29] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[30] Irina Higgins, Loic Matthew, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework. In ICLR 2017: International Conference on Learning Representations 2017, 2017.