Pseudo Label

268 papers with code • 0 benchmarks • 0 datasets

A lightweight but very power technique for semi supervised learning


Use these libraries to find Pseudo Label models and implementations

Most implemented papers

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

google-research/fixmatch 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 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

yxgeee/VisDA-ECCV20 14 Mar 2020

To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.

Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

facebookresearch/suncet 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 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 15 Feb 2023

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

layumi/Seg-Uncertainty 8 Mar 2020

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

yxgeee/SpCL NeurIPS 2020

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 4 Mar 2021

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.