{"intvolume":"        70","publist_id":"6398","volume":70,"year":"2017","isi":1,"acknowledgement":"We thank Tim Salimans for spotting a mistake in our preliminary arXiv manuscript. This work was funded by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 308036.","project":[{"name":"Lifelong Learning of Visual Scene Understanding","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036","call_identifier":"FP7"}],"has_accepted_license":"1","author":[{"first_name":"Alexander","last_name":"Kolesnikov","full_name":"Kolesnikov, Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"publication":"34th International Conference on Machine Learning","page":"1905 - 1914","external_id":{"isi":["000683309501102"],"arxiv":["1612.08185"]},"publication_identifier":{"isbn":["978-151085514-4"]},"conference":{"end_date":"2017-08-11","location":"Sydney, Australia","name":"ICML: International Conference on Machine Learning","start_date":"2017-08-06"},"citation":{"chicago":"Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” In <i>34th International Conference on Machine Learning</i>, 70:1905–14. JMLR, 2017.","apa":"Kolesnikov, A., &#38; Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling. In <i>34th International Conference on Machine Learning</i> (Vol. 70, pp. 1905–1914). Sydney, Australia: JMLR.","ista":"Kolesnikov A, Lampert C. 2017. PixelCNN models with auxiliary variables for natural image modeling. 34th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 70, 1905–1914.","ama":"Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural image modeling. In: <i>34th International Conference on Machine Learning</i>. Vol 70. JMLR; 2017:1905-1914.","short":"A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.","mla":"Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” <i>34th International Conference on Machine Learning</i>, vol. 70, JMLR, 2017, pp. 1905–14.","ieee":"A. Kolesnikov and C. Lampert, “PixelCNN models with auxiliary variables for natural image modeling,” in <i>34th International Conference on Machine Learning</i>, Sydney, Australia, 2017, vol. 70, pp. 1905–1914."},"ec_funded":1,"_id":"1000","language":[{"iso":"eng"}],"oa_version":"Submitted Version","oa":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","date_created":"2018-12-11T11:49:37Z","publisher":"JMLR","scopus_import":"1","quality_controlled":"1","title":"PixelCNN models with auxiliary variables for natural image modeling","status":"public","type":"conference","day":"01","month":"08","article_processing_charge":"No","date_updated":"2023-09-22T09:50:41Z","date_published":"2017-08-01T00:00:00Z","publication_status":"published","department":[{"_id":"ChLa"}],"main_file_link":[{"url":"https://arxiv.org/abs/1612.08185","open_access":"1"}],"abstract":[{"lang":"eng","text":"We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models. "}]}