Clustering Based Active Learning

Clustering-based active learning aims to efficiently train machine learning models by strategically selecting data points for human annotation, leveraging the inherent cluster structure within the data. Current research focuses on developing algorithms that optimize query selection, often incorporating techniques like adaptive clustering, bandit feedback, and diversity exploration to minimize annotation costs while maximizing model performance. This approach is particularly valuable in domains with expensive or time-consuming labeling processes, such as image segmentation, natural language processing, and speech recognition, offering significant potential for improving model accuracy and reducing bias with limited human intervention.

Papers

July 14, 2024

A3S: A General Active Clustering Method with Pairwise Constraints
Xun Deng, Junlong Liu, Han Zhong, Fuli Feng, Chen Shen, Xiangnan He, Jieping Ye, Zheng Wang
Semi Supervised Clustering Clustering Performance Adaptive Clustering Clustering Based Active Learning Pairwise Constraint Adaptive Aggregation

June 17, 2024

Active clustering with bandit feedback
Victor Thuot, Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen
Multi Armed Bandit Bandit Feedback UCB Algorithm Uniform Sampling Clustering Based Active Learning Stochastic Feedback

September 23, 2023

Discwise Active Learning for LiDAR Semantic Segmentation
Ozan Unal, Dengxin Dai, Ali Tamer Unal, Luc Van Gool
Semantic Segmentation Active Learning Mid Range LiDAR Lidar Semantic Segmentation LiDAR Input Clustering Based Active Learning

May 26, 2023

D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias
Sabit Hassan, Malihe Alikhani
Active Learning Bias Mitigation NLP Model Mitigating Bias Annotation System Clustering Based Active Learning

July 6, 2022

Mitigating shortage of labeled data using clustering-based active learning with diversity exploration
Xuyang Yan, Shabnam Nazmi, Biniam Gebru, Mohd Anwar, Abdollah Homaifar, Mrinmoy Sarkar, Kishor Datta Gupta
Active Learning Raw Data Density Based Clustering Based Sampling Clustering Based Active Learning Experience Shortage Scenario Bi Cluster

June 21, 2022

Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Self Supervised Learning General Analysis Dimensionality Reduction Speech Emotion Recognition Feature Space Contrastive Predictive Coding Clustering Based Active Learning Dimensional Representation

January 23, 2022

Partition-Based Active Learning for Graph Neural Networks
Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei
Graph Neural Network Active Learning Semi Supervised Learning Clustering Based Active Learning

December 26, 2021

Unsupervised Clustering Active Learning for Person Re-identification
Wenjing Gao, Minxian Li
Unsupervised Representation Learning ID Benchmark Unsupervised Re Id Supervised Person Re IDentification Clustering Based Active Learning