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 Experiment Video

Updated: Nov 25, 2025

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.3K

Sample-Guided Adaptive Class Prototype for Visual Domain Adaptation.

Chao Han1, Xiaoyang Li1, Zhen Yang1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.

Sensors (Basel, Switzerland)
|December 15, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A K-Band Four-Channel Beamformer with Temperature Compensation Based on 65 nm CMOS Process.

Micromachines·2026
Same author

Secure color image encryption using a fractional-order chaotic PRNG and DNA-level operations.

Scientific reports·2026
Same author

A 6-18 GHz High-Efficiency GaN Power Amplifier Using Transistor Stacking and Reactive Matching.

Micromachines·2026
Same author

The CT-based attenuation index of peri-bowel adipose tissue can predict disease progression in inflammatory bowel disease patients.

European radiology·2025
Same author

Electron-injected Pd@CeO<sub>2</sub> nanozymes for multifaceted ROS scavenging and protection against ischemia-reperfusion injury in skin flaps.

Journal of nanobiotechnology·2025
Same author

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

This study introduces a novel sample-guided adaptive class prototype method for domain adaptation in multi-sensor systems. It improves cross-domain distribution handling by considering sample difficulty and class diversity.

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Domain adaptation addresses data distribution mismatches in multi-sensor systems.
  • Existing methods often align distributions using statistics but neglect discriminative structures within target data.
  • This limitation impacts performance in real-world applications with varying data distributions.

Purpose of the Study:

  • To propose a novel sample-guided adaptive class prototype method for domain adaptation.
  • To overcome the limitations of traditional statistical distribution matching.
  • To enhance recognition accuracy in multi-sensor systems by better modeling target data.

Main Methods:

  • Introduced a sample-guided adaptive class prototype method with a no distribution matching strategy.
Keywords:
adaptive class prototypedomain adaptationsample selection

More Related Videos

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

707
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.1K

Related Experiment Videos

Last Updated: Nov 25, 2025

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.3K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

707
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

2.1K
  • Developed a modified nearest class prototype to allow intra-class diversity while preserving discrimination.
  • Implemented an easy-to-hard testing scheme to leverage easily classified samples for harder ones.

    • Main Results:

    • The proposed method effectively handles cross-domain distribution mismatches without direct statistical alignment.
    • Modified nearest class prototypes improved class discrimination and diversity.
    • The easy-to-hard testing scheme enhanced the prediction of difficult target samples.
    • Extensive experiments validated the superior performance of the proposed approach.

    Conclusions:

    • The sample-guided adaptive class prototype method offers a robust solution for domain adaptation.
    • This approach enhances multi-sensor system performance by better utilizing target sample information.
    • The findings suggest a new direction for developing more effective domain adaptation techniques.