Results 31 to 40 of about 7,687 (182)

Uncertainty Estimation in One-Stage Object Detection

, 2020
Environment perception is the task for intelligent vehicles on which all subsequent steps rely. A key part of perception is to safely detect other road users such as vehicles, pedestrians, and cyclists.
Dietmayer, Klaus, Kraus, Florian
core   +1 more source

Uncertainty estimation for deep neural networks to improve the assessment of plumage conditions of chickens

Smart Agricultural Technology, 2023
The combination of computer vision with deep learning has become a popular tool for automation of labor-intensive monitoring tasks in modern livestock farming.
Christian Lamping, Gert Kootstra, Marjolein Derks   +2 more
doaj   +1 more source

Crowd Counting with Decomposed Uncertainty

, 2020
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed.
Oh, Min-hwan, Olsen, Peder A., Ramamurthy, Karthikeyan Natesan   +2 more
core   +1 more source

Evaluating Aleatoric Uncertainty via Conditional Generative Models

, 2022
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications. Previous research on aleatoric uncertainty estimation mainly targets closed-formed conditional densities or variances, which requires strong restrictions on
Huang, Ziyi, Lam, Henry, Zhang, Haofeng
openaire   +2 more sources

Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles

Entropy, 2023
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state.
Timothy Davey
doaj   +1 more source

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

, 2018
To assure that an autonomous car is driving safely on public roads, its object detection module should not only work correctly, but show its prediction confidence as well.
Dietmayer, Klaus, Feng, Di, Rosenbaum, Lars   +2 more
core   +1 more source

Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression

Neural Processing Letters, 2022
AbstractA Bayesian treatment of deep learning allows for the computation of uncertainties associated with the predictions of deep neural networks. We show how the concept of Errors-in-Variables can be used in Bayesian deep regression to also account for the uncertainty associated with the input of the employed neural network.
J. Martin, C. Elster
openaire   +2 more sources

Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines

IEEE Open Journal of Engineering in Medicine and Biology
Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the ...
Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte   +2 more
doaj   +1 more source

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

, 2017
Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly
Bhattacharyya, Apratim, Fritz, Mario, Schiele, Bernt   +2 more
core   +1 more source

CAPO: Causal-Adaptive Preference Optimization for Diffusion Models via Causal Inference Uncertainty

IEEE Access
Post-training alignment of diffusion models based on human feedback uniformly applies equal weights to all preference pairs, ignoring the inherent aleatoric and epistemic uncertainty in feedback, leading to overfitting on noisy signals and insufficient ...
Sihan Hu
doaj   +1 more source