WebSep 12, 2024 · In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory. More … WebNov 20, 2024 · MIT researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision …
Evidential Deep Learning to Quantify Classification …
WebSelect search scope, currently: articles+ all catalog, articles, website, & more in one search; catalog books, media & more in the Stanford Libraries' collections; articles+ journal articles & other e-resources WebApr 1, 2024 · In deep evidential regression, Amini et al. [13] related this conjugate prior to evidential deep learning and defined the total evidence, Φ = 2 v + α, to support the parameter estimation. Following the deep evidential regression framework, we train a network to infer the hyper-parameters m = (γ, v, α, β) of the NIG evidential distribution ... snavely lumber
Evidential Deep Learning - GitHub
WebApr 1, 2024 · Although deep learning-based stereo matching approaches have achieved excellent performance in recent years, it is still a non-trivial task to estimate the uncertainty of the produced disparity map.In this paper, we propose a novel approach to estimate both aleatoric and epistemic uncertainties for stereo matching in an end-to-end way. We … WebNov 1, 2024 · In this work, based on the small-scale, multi-modality, incompleteness characteristics of medical image data, we propose a deep evidential fusion method for multi-modality medical image classification tasks. The experimental results show that the Dempster-Shafer theory could be a qualified framework for information fusion in deep … WebIn this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias ... snavely attorney