{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:40:55Z","timestamp":1769856055747,"version":"3.49.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031287183","type":"print"},{"value":"9783031287190","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-28719-0_3","type":"book-chapter","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T18:03:51Z","timestamp":1679421831000},"page":"32-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Disentangling Shape and\u00a0Pose for\u00a0Object-Centric Deep Active Inference Models"],"prefix":"10.1007","author":[{"given":"Stefano","family":"Ferraro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toon","family":"Van de Maele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Mazzaglia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Verbelen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart","family":"Dhoedt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798\u20131828 (2013). https:\/\/doi.org\/10.1109\/TPAMI.2013.50","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Billard, A., Kragic, D.: Trends and challenges in robot manipulation. Science 364, eaat8414 (2019). https:\/\/doi.org\/10.1126\/science.aat8414","DOI":"10.1126\/science.aat8414"},{"key":"3_CR3","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. Technical report. arXiv:1512.03012 [cs.GR], Stanford University \u2013 Princeton University \u2013 Toyota Technological Institute at Chicago (2015)"},{"key":"3_CR4","unstructured":"Chen, R.T.Q., Li, X., Grosse, R., Duvenaud, D.: Isolating sources of disentanglement in VAEs. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 2615\u20132625. Curran Associates Inc., Red Hook (2018)"},{"issue":"4","key":"3_CR5","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1109\/TPAMI.2016.2567384","volume":"39","author":"A Dosovitskiy","year":"2017","unstructured":"Dosovitskiy, A., Springenberg, J.T., Tatarchenko, M., Brox, T.: Learning to generate chairs, tables and cars with convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 692\u2013705 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2567384","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Eslami, S.M.A., et al.: Neural scene representation and rendering. Science 360(6394), 1204\u20131210 (2018). https:\/\/doi.org\/10.1126\/science.aar6170. https:\/\/www.science.org\/doi\/10.1126\/science.aar6170","DOI":"10.1126\/science.aar6170"},{"key":"3_CR7","unstructured":"Fountas, Z., Sajid, N., Mediano, P., Friston, K.: Deep active inference agents using Monte-Carlo methods. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 11662\u201311675. Curran Associates, Inc. (2020)"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Hawkins, J., Ahmad, S., Cui, Y.: A theory of how columns in the neocortex enable learning the structure of the world. Front. Neural Circuits 11, 81 (2017). https:\/\/doi.org\/10.3389\/fncir.2017.00081. http:\/\/journal.frontiersin.org\/article\/10.3389\/fncir.2017.00081\/full","DOI":"10.3389\/fncir.2017.00081"},{"key":"3_CR9","unstructured":"Higgins, I., et al.: Beta-VAE: learning basic visual concepts with a constrained variational framework. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings (2017)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"3_CR11","unstructured":"Kim, H., Mnih, A.: Disentangling by factorising. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2649\u20132658. PMLR (2018)"},{"key":"3_CR12","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv:1312.6114 [cs, stat] (2014)"},{"key":"3_CR13","unstructured":"Kumar, A., Sattigeri, P., Balakrishnan, A.: Variational inference of disentangled latent concepts from unlabeled observations. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April\u20133 May 2018, Conference Track Proceedings (2018)"},{"key":"3_CR14","unstructured":"Lanillos, P., et al.: Active inference in robotics and artificial agents: survey and challenges (2021)"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI 2018\/IAAI 2018\/EAAI 2018. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.12278"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Van de Maele, T., Verbelen, T., Catal, O., Dhoedt, B.: Disentangling what and where for 3D object-centric representations through active inference. arXiv:2108.11762 [cs] (2021)","DOI":"10.1007\/978-3-030-93736-2_50"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Van de Maele, T., Verbelen, T., \u00c7atal, O., De Boom, C., Dhoedt, B.: Active vision for robot manipulators using the free energy principle. Front. Neurorobotics 15, 642780 (2021). https:\/\/doi.org\/10.3389\/fnbot.2021.642780. https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2021.642780\/full","DOI":"10.3389\/fnbot.2021.642780"},{"key":"3_CR18","doi-asserted-by":"publisher","unstructured":"Van de Maele, T., Verbelen, T., \u00c7atal, O., Dhoedt, B.: Embodied object representation learning and recognition. Front. Neurorobotics 16 (2022). https:\/\/doi.org\/10.3389\/fnbot.2022.840658. https:\/\/www.frontiersin.org\/article\/10.3389\/fnbot.2022.840658","DOI":"10.3389\/fnbot.2022.840658"},{"key":"3_CR19","doi-asserted-by":"publisher","unstructured":"Mazzaglia, P., Verbelen, T., \u00c7atal, O., Dhoedt, B.: The free energy principle for perception and action: a deep learning perspective. Entropy 24(2) (2022). https:\/\/doi.org\/10.3390\/e24020301. https:\/\/www.mdpi.com\/1099-4300\/24\/2\/301","DOI":"10.3390\/e24020301"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. arXiv:1812.03828 [cs] (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. arXiv:2003.08934 [cs] (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/0166-2236(83)90190-X","volume":"6","author":"M Mishkin","year":"1983","unstructured":"Mishkin, M., Ungerleider, L.G., Macko, K.A.: Object vision and spatial vision: two cortical pathways. Trends Neurosci. 6, 414\u2013417 (1983)","journal-title":"Trends Neurosci."},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. arXiv:1901.05103 [cs] (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Parr, T., Sajid, N., Da Costa, L., Mirza, M.B., Friston, K.J.: Generative models for active vision. Front. Neurorobotics 15, 651432 (2021). https:\/\/doi.org\/10.3389\/fnbot.2021.651432. https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2021.651432\/full","DOI":"10.3389\/fnbot.2021.651432"},{"key":"3_CR25","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv:1401.4082 [cs, stat] (2014)"},{"key":"3_CR26","unstructured":"Rezende, D.J., Viola, F.: Taming VAEs. arXiv:1810.00597 [cs, stat] (2018)"},{"key":"3_CR27","doi-asserted-by":"publisher","unstructured":"Sancaktar, C., van Gerven, M.A.J., Lanillos, P.: End-to-end pixel-based deep active inference for body perception and action. In: 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (2020). https:\/\/doi.org\/10.1109\/icdl-epirob48136.2020.9278105","DOI":"10.1109\/icdl-epirob48136.2020.9278105"},{"key":"3_CR28","unstructured":"Sitzmann, V., Martel, J.N.P., Bergman, A.W., Lindell, D.B., Wetzstein, G.: SIREN: implicit neural representations with periodic activation functions. arXiv:2006.09661 [cs, eess] (2020)"},{"key":"3_CR29","unstructured":"van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O.: Are disentangled representations helpful for abstract visual reasoning? In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/bc3c4a6331a8a9950945a1aa8c95ab8a-Paper.pdf"},{"issue":"6","key":"3_CR30","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s00422-018-0785-7","volume":"112","author":"K Ueltzh\u00f6ffer","year":"2018","unstructured":"Ueltzh\u00f6ffer, K.: Deep active inference. Biol. Cybern. 112(6), 547\u2013573 (2018). https:\/\/doi.org\/10.1007\/s00422-018-0785-7","journal-title":"Biol. Cybern."},{"issue":"4","key":"3_CR31","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR32","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016). https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/44f683a84163b3523afe57c2e008bc8c-Paper.pdf"},{"key":"3_CR33","doi-asserted-by":"publisher","unstructured":"\u00c7atal, O., Wauthier, S., De Boom, C., Verbelen, T., Dhoedt, B.: Learning generative state space models for active inference. Front. Comput. Neurosci. 14, 574372 (2020). https:\/\/doi.org\/10.3389\/fncom.2020.574372. https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2020.574372\/full","DOI":"10.3389\/fncom.2020.574372"}],"container-title":["Communications in Computer and Information Science","Active Inference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28719-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T18:11:21Z","timestamp":1679422281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28719-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031287183","9783031287190"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28719-0_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Active Inference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwai-ws2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwaiworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}