Pseudo Label
268 papers with code • 0 benchmarks • 0 datasets
A lightweight but very power technique for semi supervised learning
Benchmarks
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Libraries
Use these libraries to find Pseudo Label models and implementationsMost implemented papers
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
google-research/fixmatch
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NeurIPS 2020
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
End-to-End Semi-Supervised Object Detection with Soft Teacher
microsoft/SoftTeacher
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ICCV 2021
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
Webly Supervised Image Classification with Self-Contained Confidence
bigvideoresearch/SCC
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ECCV 2020
Therefore, a simple yet effective WSL framework is proposed.
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
facebookresearch/suncet
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ICCV 2021
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS).
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
dmcv-ecnu/MindSpore_ModelZoo
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AAAI 2021
Firstly, we construct a pretext task, \textit{i. e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network.
Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
AlibabaResearch/efficientteacher
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The Pseudo Label Assigner prevents the occurrence of bias caused by a large number of low-quality pseudo labels that may interfere with the Dense Detector during the student-teacher mutual learning mechanism, and the Epoch Adaptor utilizes domain and distribution adaptation to allow Dense Detector to learn globally distributed consistent features, making the training independent of the proportion of labeled data.
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory.
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
HiLab-git/DTC
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Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions.
