{"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/bayesian-convolutional-neural-networks-with-1","title":"Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference","arxiv_id":"1806.05978","date":"2018-06-15","proceeding":null,"authors":["Kumar Shridhar","Felix Laumann","Marcus Liwicki"],"abstract":"We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus function in the final layer, we estimate aleatoric and epistemic uncertainty in a coherent manner. The intractable posterior probability distributions over weights are inferred by Bayes by Backprop. Firstly, we demonstrate how this reliable variational inference method can serve as a fundamental construct for various network architectures. On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally. Secondly, we examine how our proposed measure for aleatoric and epistemic uncertainties is derived and validate it on the aforementioned datasets.","url_abs":"https://arxiv.org/abs/1806.05978v6","url_pdf":"https://arxiv.org/pdf/1806.05978v6.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":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/Anou9531/Bayesian-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/kumar-shridhar/BayesianConvNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/kumar-shridhar/PyTorch-BayesianCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/liqichen6688/baycnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/nomercy77/Implementing-Bayesian-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/MindSpore-scientific-2/code-1/tree/main/Uncertainty_Calibration_Object_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/MindSpore-scientific-2/code-11/tree/main/Uncertainty_Calibration_Object_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/MindSpore-scientific-2/code-2/tree/main/Uncertainty_Calibration_Object_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"bayesian-convolutional-neural-networks-with-1","repo_url":"https://github.com/MindSpore-scientific-2/code-8/tree/main/Uncertainty_Calibration_Object_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.05978","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}