Cover Image

InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Zhe, Yu, Xiaowei, Zhu, Dajiang, Hughes, Michael C.
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis
by: Huang, Zhe, et al.
Published: (2024)

Semi-Supervised Crowd Counting from Unlabeled Data
by: Duan, Haoran, et al.
Published: (2021)

PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision
by: Wang, Chengjie, et al.
Published: (2024)

Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
by: Shen, Aoran, et al.
Published: (2024)

Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
by: Roy, Shuvendu, et al.
Published: (2023)

GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data
by: Zhao, Haochen, et al.
Published: (2024)

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning
by: Duan, Yue, et al.
Published: (2025)

Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
by: Fan, Heng-Bo, et al.
Published: (2024)

Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
by: Elbatel, Marawan, et al.
Published: (2024)

OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
by: Niu, Shengjie, et al.
Published: (2024)

Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
by: Akiba, Kohki, et al.
Published: (2026)

Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
by: Huang, Zhe, et al.
Published: (2023)

FATE: A Prompt-Tuning-Based Semi-Supervised Learning Framework for Extremely Limited Labeled Data
by: Liu, Hezhao, et al.
Published: (2025)

NoisyNN: Exploring the Impact of Information Entropy Change in Learning Systems
by: Yu, Xiaowei, et al.
Published: (2023)

Supercm: Revisiting Clustering for Semi-Supervised Learning
by: Singh, Durgesh, et al.
Published: (2025)

Adaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled Learning
by: Yamane, Takehiro, et al.
Published: (2025)

Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization
by: Lee, Dongkwan, et al.
Published: (2025)

Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
by: Wang, Tao, et al.
Published: (2024)

SINCERE: Supervised Information Noise-Contrastive Estimation REvisited
by: Feeney, Patrick, et al.
Published: (2023)

VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled Examples in Semi-supervised Learning
by: Fang, Shijie, et al.
Published: (2024)

Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling
by: Lyu, Zongyao, et al.
Published: (2024)

AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
by: Wang, Haonan, et al.
Published: (2024)

Learning Style Identification Using Semi-Supervised Self-Taught Labeling
by: Ayyoub, Hani Y., et al.
Published: (2024)

Inter-Domain Mixup for Semi-Supervised Domain Adaptation
by: Li, Jichang, et al.
Published: (2024)

Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
by: Shao, Qian, et al.
Published: (2024)

Systematic comparison of semi-supervised and self-supervised learning for medical image classification
by: Huang, Zhe, et al.
Published: (2023)

From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
by: Gabetni, Firas, et al.
Published: (2026)

Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Training
by: Xiao, Huaiyuan, et al.
Published: (2025)

RegMixMatch: Optimizing Mixup Utilization in Semi-Supervised Learning
by: Han, Haorong, et al.
Published: (2024)

Learning to Predict Gradients for Semi-Supervised Continual Learning
by: Luo, Yan, et al.
Published: (2022)

Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
by: Zhang, Jiahan, et al.
Published: (2024)

Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
by: Liang, Jiachen, et al.
Published: (2024)

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels
by: Yann, Rui, et al.
Published: (2025)

Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data
by: Acharya, Anish, et al.
Published: (2024)

Sampling Control for Imbalanced Calibration in Semi-Supervised Learning
by: Tian, Senmao, et al.
Published: (2025)

Semi-Supervised Learning with Multi-Head Co-Training
by: Chen, Mingcai, et al.
Published: (2021)

The Impact of Semi-Supervised Learning on Line Segment Detection
by: Engman, Johanna, et al.
Published: (2024)

Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation
by: Jiang, Xixi, et al.
Published: (2024)

Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
by: Paul, Sneha, et al.
Published: (2024)

PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning
by: Tian, Bowen, et al.
Published: (2024)