{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/encoding-the-latent-posterior-of-bayesian","title":"Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification","arxiv_id":"2012.02818","date":"2020-12-04","proceeding":null,"authors":["Gianni Franchi","Andrei Bursuc","Emanuel Aldea","Severine Dubuisson","Isabelle Bloch"],"abstract":"Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior distribution of the network parameters, most BNN approaches are either limited to small networks or rely on constraining assumptions such as parameter independence. These drawbacks have enabled prominence of simple, but computationally heavy approaches such as Deep Ensembles, whose training and testing costs increase linearly with the number of networks. In this work we aim for efficient deep BNNs amenable to complex computer vision architectures, e.g. ResNet50 DeepLabV3+, and tasks, e.g. semantic segmentation, with fewer assumptions on the parameters. We achieve this by leveraging variational autoencoders (VAEs) to learn the interaction and the latent distribution of the parameters at each network layer. Our approach, Latent-Posterior BNN (LP-BNN), is compatible with the recent BatchEnsemble method, leading to highly efficient ({in terms of computation and} memory during both training and testing) ensembles. LP-BNN s attain competitive results across multiple metrics in several challenging benchmarks for image classification, semantic segmentation and out-of-distribution detection.","url_abs":"https://arxiv.org/abs/2012.02818v2","url_pdf":"https://arxiv.org/pdf/2012.02818v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"encoding-the-latent-posterior-of-bayesian","repo_url":"https://github.com/ENSTA-U2IS-AI/torch-uncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"encoding-the-latent-posterior-of-bayesian","repo_url":"https://github.com/giannifranchi/LP_BNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"decision-making-under-uncertainty","task_name":"Decision Making Under Uncertainty"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"deep-ensembles","method_name":"Deep Ensembles"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-cifar-10","task":"Image Classification","dataset":"CIFAR-10","model":"LP-BNN (ours) + cutout","rank_in_archive_order":145,"of":265,"metrics":{"Percentage correct":"95.02"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-cifar-100","task":"Image Classification","dataset":"CIFAR-100","model":"LP-BNN (ours) + cutout","rank_in_archive_order":146,"of":211,"metrics":{"Percentage correct":"76.85"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2012.02818","atlas_url":"https://app.syntology.ai/?focus=2012.02818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.02818"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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