Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cluster Sampling Method

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...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Vesicular Tubular Clusters

Vesicular Tubular Clusters

After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...

Collisions in Multiple Dimensions: Problem Solving

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

You might also read

Related Articles

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

Sort by

Same author

APSevLM: Acute Pancreatitis Severity Language Model.

IEEE journal of biomedical and health informatics·2026

Same author

Chemical-Disease-Gene Association Prediction based on Pretraining-Prompt-Finetuning Heterogeneous Graph Neural Network for Drug Discovery.

IEEE journal of biomedical and health informatics·2026

Same author

Graph-Embedded Deep Generative Clustering for Single-Cell Multi-Omics Data Integration.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

A unified framework for sequential recommendation with gated differential amplified attention and repetition-exploration intent modeling.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Multiple interpretation ensemble distillation for graph neural networks.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

ID-Guided Multimodal experts with contrastive diffusion for sequential recommendation.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

DNA: Improving text-based person search through distillation learning, negated relation-aware learning, and augmented representation learning.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

MCFusion-DDI: Multimodal cross-attention fusion of local-global features and latent drug associations for explainable DDI prediction.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Multiscale EEG feature fusion for recognizing 3D object shapes through active touch.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Fixed/preassigned-time stabilization and time-energy tradeoff analysis of delayed memristive reaction diffusion neural networks.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Fourier multi-component and multi-layer neural networks: Unlocking high-frequency potential.

Neural networks : the official journal of the International Neural Network Society·2026

See all related articles

Search research articles

Home
Pyramid Contrastive Learning For Clustering.

Home
Pyramid Contrastive Learning For Clustering.

Related Experiment Video

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

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

Published on: February 15, 2017

Pyramid contrastive learning for clustering.

Zi-Feng Zhou¹, Dong Huang², Chang-Dong Wang³

¹College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 7, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces Pyramid Contrastive Learning for Clustering (PCLC), a novel deep clustering method that enhances representation learning by integrating multi-layer contrastive analysis and a hybrid CNN-Transformer architecture for improved image clustering.

Keywords:

CNN-transformer encoder Contrastive clustering Data clustering Deep clustering Image clustering

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Related Experiment Videos

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

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

Published on: February 15, 2017

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Deep clustering methods leverage deep neural networks for joint representation learning and clustering.
Existing methods often overlook sample-wise contrastiveness, rely on single-layer features, and struggle with global dependencies due to reliance on Convolutional Neural Networks (CNNs).

Purpose of the Study:

To address limitations in current deep clustering techniques.
To propose a novel method, Pyramid Contrastive Learning for Clustering (PCLC), for enhanced discriminative representation learning and clustering.

Main Methods:

PCLC employs a pyramidal contrastive architecture for joint contrastive learning and clustering across multiple network layers.
A hybrid CNN-Transformer encoder captures both local and global image dependencies.

Simultaneous instance-level and cluster-level twin contrastive learning is performed across multiple stages.

Main Results:

PCLC demonstrates superior clustering performance on challenging image datasets compared to state-of-the-art methods.
The method effectively integrates multi-stage feature learning and contrastive objectives.

Conclusions:

PCLC offers a significant advancement in deep clustering by effectively combining multi-layer contrastive learning with a hybrid CNN-Transformer backbone.
The proposed approach enhances discriminative representation learning for improved image clustering outcomes.