138 BIBLIOGRAPHY A., Lengauer, S., Licandro, R., Nguyen, D.-H., Nguyen-Ho, T.-L., Perez Rey, L., Pham, B.-D., Pham, M.-K., Preiner, R., Schreck, T., Trinh, Q.-H., Tonnaer, L., von Tycowicz, C., and Vu-Le, T.-A. (2021). SHREC 2021: Retrieval of cultural heritage objects. Computers and Graphics (Pergamon), 100. (Cited on pages 53, 93, 94, 95, 99, and 103.) Soatto, S. (2011). Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control. arXiv preprint arxiv.1110.2053. (Cited on page 54.) Sosnovik, I., Szmaja, M., and Smeulders, A. (2020). Scale-Equivariant Steerable Networks. In8th International Conference on Learning Representations (ICLR 2020). (Cited on page 57.) Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015). Multi-view Convolutional Neural Networks for 3D Shape Recognition. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter:945 – 953. (Cited on page 96.) Tomczak, J. M. (2022). Deep Generative Modeling. Springer International Publishing. (Cited on page 5.) Tonnaer, L., Holenderski, M., and Menkovski, V. (2023). Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations. InAdvances in Intelligent Data Analysis XXI (IDA). Springer, Cham. Tonnaer, L., Li, J., Osin, V., Holenderski, M., and Menkovski, V. (2019). Anomaly Detection for Visual Quality Control of 3D-Printed Products. InProceedings of the International Joint Conference on Neural Networks (IJCNN). Tonnaer, L., Pérez Rey, L. A., Menkovski, V., Holenderski, M., and Portegies, J. W. (2022). Quantifying and Learning Linear Symmetry-Based Disentanglement. InProceedings of the 39th International Conference on Machine Learning (ICML). (Cited on page 109.) Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., Schölkopf, B., and Bauer, S. (2021). On Disentangled Representations Learned from Correlated Data. 38th International Conference on Machine Learning (ICML 2021). (Cited on pages 7, 8, 106, and 109.) Wang, Z., Dai, B., Wipf, D., and Zhu, J. (2020). Further Analysis of Outlier Detection with Deep Generative Models. In1st I Can’t Believe It’s Not Better Workshop (ICBINB @ NeurIPS 2020). (Cited on page 126.)
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