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Pseudo Label

A lightweight but very power technique for semi supervised learning

Papers

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Showing 476–500 of 956 papers

TitleStatusHype
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text ClusteringCode1
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation—0
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble TrainingCode1
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype LearningCode0
SAD: Semi-Supervised Anomaly Detection on Dynamic GraphsCode1
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking—0
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification—0
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment—0
Rethinking Semi-supervised Learning with Language ModelsCode1
Pseudo-Label Training and Model Inertia in Neural Machine Translation—0
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation—0
Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic SegmentationCode1
Contrastive Mean Teacher for Domain Adaptive Object DetectorsCode1
High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling—0
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label LearningCode1
Semi-supervised Domain Adaptation via Prototype-based Multi-level LearningCode1
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification—0
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation—0
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning SystemCode0
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training—0
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI—0
Test-Time Adaptation with Perturbation Consistency Learning—0
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