pseudo label

139 papers with code • 0 benchmarks • 0 datasets

A lightweight but very power technique for semi supervised learning

Libraries

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).

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.

Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification

yxgeee/MMT ICLR 2020

In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.

Weakly supervised discriminative feature learning with state information for person identification

KovenYu/state-information CVPR 2020

We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

danikiyasseh/SoQal 20 Apr 2020

One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.