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Conference Paper

Generalization and Robustness Implications in Object-Centric Learning

MPS-Authors

Dittadi ,  Andrea
External Organizations;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  Bernhard       
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://proceedings.mlr.press/v162/dittadi22a.html
(Publisher version)

https://doi.org/10.48550/arXiv.2107.00637
(Preprint)

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Citation

Dittadi, A., Papa, S., De Vita, M., Schölkopf, B., Winther, O., & Locatello, F. (2022). Generalization and Robustness Implications in Object-Centric Learning. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (pp. 5221-5285). PMLR. Retrieved from https://proceedings.mlr.press/v162/dittadi22a.html.


Cite as: https://hdl.handle.net/21.11116/0000-0010-2F78-4
Abstract
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