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

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

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

Sort by
Same author

Electricity generation from food wastes and microbial community structure in microbial fuel cells.

Bioresource technology·2013
Same author

Plasmon-enhanced photothermoelectric conversion in chemical vapor deposited graphene p-n junctions.

Journal of the American Chemical Society·2013
Same author

Rapid and sensitive detection of vinorelbine in the urine of tumor patients by capillary electrophoresis with tris(2,2'-bipyridyl)ruthenium(II)-based electrochemiluminescence assay.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2013
Same author

Synthesis and biological evaluation of 4-phenoxy-6,7-disubstituted quinolines possessing semicarbazone scaffolds as selective c-Met inhibitors.

Archiv der Pharmazie·2013
Same author

The effects of external electric field: creating non-zero first hyperpolarizability for centrosymmetric benzene and strongly enhancing first hyperpolarizability for non-centrosymmetric edge-modified graphene ribbon H2N-(3,3)ZGNR-NO2.

Journal of molecular modeling·2013
Same author

Msx2 plays a critical role in lens epithelium cell cycle control.

International journal of ophthalmology·2013
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
Same journal

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
See all related articles
  1. Home
  3. Efficient Image Debiased Contrastive Clustering.
  1. Home
  3. Efficient Image Debiased Contrastive Clustering.

Related Experiment Video

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Efficient Image Debiased Contrastive Clustering.

Hongjie Jia, Junyi Chen, Qirong Mao

    IEEE Transactions on Neural Networks and Learning Systems
    |June 23, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Debiased contrastive clustering (DCC) offers efficient image clustering by integrating differential augmentations and refined sampling. This lightweight model achieves state-of-the-art accuracy without large models or high computational costs.

    Related Experiment Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • E-commerce and social media generate vast image data, challenging real-time clustering and recommendation systems.
    • Existing multistage or large-pretrained-model (LPM) clustering methods offer high accuracy but incur significant computational costs and large model sizes.
    • Single-stage methods are resource-efficient but struggle with limited feature diversity and accuracy issues due to false positives/negatives.

    Purpose of the Study:

    • To develop an efficient and lightweight clustering model for image data.
    • To address the limitations of existing single-stage and multistage clustering methods.
    • To improve accuracy and efficiency in real-time image clustering and recommendation.

    Main Methods:

  • Proposed Debiased Contrastive Clustering (DCC), an efficient lightweight model.
  • Integrated differential augmentations and refined sampling for enhanced feature representation.
  • Employed debiased contrastive loss to minimize false negatives and pseudo-labels with consistency regularization to mitigate false positives.

    • Main Results:

    • DCC demonstrated superior performance across seven challenging datasets compared to state-of-the-art (SOTA) methods.
    • Achieved higher accuracy, normalized mutual information (NMI), and adjusted Rand index (ARI).
    • Exhibited faster convergence and improved overall efficiency without reliance on multistage training or LPMs.

    Conclusions:

    • DCC effectively balances accuracy and efficiency in image clustering.
    • The proposed methods overcome limitations of existing approaches, offering a viable solution for large-scale image data.
    • DCC provides a promising direction for real-time clustering and recommendation systems in e-commerce and social media.