Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Self-Discrepancy Theory02:45

Self-Discrepancy Theory

18.4K
One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
18.4K
Survival Tree01:19

Survival Tree

132
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
132
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

506
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
506
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Mean Absolute Deviation01:13

Mean Absolute Deviation

2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Expression of tissue factor pathway inhibitor-2 in gastric stromal tumor and its clinical significance.

Experimental and therapeutic medicine·2014
Same author

Facile access to cytocompatible multicompartment micelles with adjustable Janus-cores from A-block-B-graft-C terpolymers prepared by combination of ROP and ATRP.

Colloids and surfaces. B, Biointerfaces·2014
Same author

Functional layers for Zn(II) ion detection: from molecular design to optical fiber sensors.

The journal of physical chemistry. B·2013
Same author

Expression of the 78 kD glucose-regulated protein is induced by endoplasmic reticulum stress in the development of hepatopulmonary syndrome.

Gene·2013
Same author

Multi-nuclear silver(I) and copper(I) complexes: a novel bonding mode for bispyridylpyrrolides.

Dalton transactions (Cambridge, England : 2003)·2013
Same author

Transcriptome profilings of female Schistosoma japonicum reveal significant differential expression of genes after pairing.

Parasitology research·2013

Related Experiment Video

Updated: Aug 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

667

Adversarial style discrepancy minimization for unsupervised domain adaptation.

Xin Luo1, Wei Chen1, Zhengfa Liang2

  • 1College of Computer Science, National University of Defense Technology, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Adversarial Style Discrepancy Minimization (ASDM) for unsupervised domain adaptation. ASDM effectively addresses fine-grained domain discrepancies without auxiliary models, improving performance on key benchmarks.

Keywords:
Domain adaptationTransfer learning

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.4K

Related Experiment Videos

Last Updated: Aug 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

667
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.4K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Unsupervised domain adaptation (UDA) commonly uses adversarial learning for global feature distribution alignment.
  • Existing methods often overlook fine-grained domain discrepancies and require auxiliary models, increasing computational cost.

Purpose of the Study:

  • To propose a novel UDA method that differentiates individual samples without extra models.
  • To introduce a new metric, 'style discrepancy,' for distinguishing target samples.
  • To develop an adversarial paradigm for minimizing style discrepancy for fine-grained adaptation.

Main Methods:

  • Proposes Adversarial Style Discrepancy Minimization (ASDM) for UDA.
  • Employs a novel 'style discrepancy' metric to identify and adapt hard-to-classify samples.
  • Utilizes an adversarial approach: maximizing style discrepancy to train the classifier and minimizing it to train the feature extractor.

Main Results:

  • ASDM achieves 46.9% mIoU on the GTA5 to Cityscapes benchmark.
  • ASDM reaches 84.7% accuracy on the VisDA-2017 benchmark.
  • Demonstrates superior performance compared to existing adversarial-learning-based UDA methods without auxiliary models.

Conclusions:

  • ASDM enables effective fine-grained domain adaptation by focusing on individual sample discrepancies.
  • The proposed method significantly improves UDA performance without additional computational overhead.
  • ASDM offers a promising direction for addressing domain shift challenges in computer vision tasks.