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Pseudo Label

A lightweight but very power technique for semi supervised learning

Papers

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Showing 326–350 of 956 papers

TitleStatusHype
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression RecognitionCode0
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-IdentificationCode0
P^2OT: Progressive Partial Optimal Transport for Deep Imbalanced ClusteringCode1
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation—0
Unsupervised multi-source domain adaptation for person re-identification via feature fusion and pseudo-label refinement—0
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-IdentificationCode2
Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object DetectionCode0
Uncertainty-aware Sampling for Long-tailed Semi-supervised LearningCode0
Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning—0
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic SegmentationCode1
Data-Free Quantization via Pseudo-label Filtering—0
Uncertainty-Guided Never-Ending Learning to Drive—0
Improved Self-Training for Test-Time AdaptationCode0
PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process GuidanceCode0
SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label—0
Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change CaptioningCode0
Federated Continual Novel Class Learning—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object DetectionCode1
Student as an Inherent Denoiser of Noisy Teacher—0
Pixel-Superpixel Contrastive Learning and Pseudo-Label Correction for Hyperspectral Image Clustering—0
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMixCode1
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions—0
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