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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

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Related Experiment Video

Updated: Jul 12, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Uncertainty aware stochastic sampling for efficient object detection.

Csanád Levente Balogh1,2, Bence Pap1,2, Bálint Kővári1,3

  • 1Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary.

Scientific Reports
|July 9, 2026
PubMed
Summary

This study introduces a new method to prioritize training data for object detection models, improving accuracy by focusing on relevant samples. This approach enhances model learning and performance with minimal computational cost.

Keywords:
Computer visionObject detectionSampling efficiencySupervised learning

Related Experiment Videos

Last Updated: Jul 12, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning model effectiveness hinges on training data quality.
  • Traditional methods assume uniform data sample importance, ignoring informational content variations.
  • Object detection requires accurate classification and bounding-box regression.

Purpose of the Study:

  • To introduce a novel, computationally lightweight data prioritization methodology for object detection.
  • To dynamically adjust training data sampling probability based on relevance during learning.
  • To improve classification and localization accuracy in object detection models.

Main Methods:

  • Developed a Relative Detection Error (RDE) metric to quantify temporal instability in joint classification-regression.
  • Utilized RDE to identify samples with higher learning value.
  • Implemented an exploration-regularized stochastic sampling policy guided by RDE.

Main Results:

  • Prioritizing high-value samples led to improved F1 scores and mean Average Precision.
  • The method demonstrated more efficient and stable convergence during training.
  • Consistent improvements and strong generalization were observed across YOLO architectures and diverse datasets.

Conclusions:

  • The proposed data prioritization methodology enhances object detection performance.
  • RDE effectively identifies valuable training samples, improving learning efficiency.
  • The approach offers a lightweight, seamlessly integrable solution for standard training pipelines.