https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
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Pseudo Label
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A lightweight but very power technique for semi supervised learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Raykoooo/IAST
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/River-sh/A-robust-unsupervised-small-area-change-detection
Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
https://github.com/S-HuaBomb/FixMatch-Paddle
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Sakura03/SemiMeta
https://github.com/SaraAmd/Semi-Supervised-Learning
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/ThomasWestfechtel/GSDE
Gradual Source Domain Expansion for Unsupervised Domain Adaptation
https://github.com/Trayan7/FixMatch-with-Soft-Labels
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Valentyn1997/semi-supervised-text-classification
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Zhongying-Deng/NorMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/amazon-research/bigdetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/amazon-research/exponential-moving-average-normalization
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/amazon-science/bigdetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/angienikki/openwusu
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/beresandras/semisupervised-classification-keras
https://github.com/cardwing/codes-for-pvkd
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
https://github.com/cenkbircanoglu/isim
https://github.com/chengding0713/awesome-ppg-af-detection
https://github.com/chengglmotto/AGC-DRR
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
https://github.com/chenhaomingbob/WS3-mindspore
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
https://github.com/chongweiliu/DUT-USEG
https://github.com/detectiveli/PSMA
https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/WS3_MindSpore
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
https://github.com/facebookresearch/msn
https://github.com/fbuchert/fixmatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/frednam93/MDFD-SED
https://github.com/gomezzz/MSMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/gomezzz/distmsmatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/google-research/crest
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/harlanhong/CVPR2020-Semi-Point
https://github.com/hattrickcr7/SoftTeacher
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/hik-lab/ssod
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/hikvision-research/SSOD
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/jdauba/Domain-Adaptation-SVHN-MNIST
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
https://github.com/johnchenresearch/SSL
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.