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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/1180300419/imperfect-deweathering

공식 코드

Learning Real-World Image De-Weathering with Imperfect Supervision

https://github.com/2han9x1a0release/RLCC

공식 코드

Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification

https://github.com/4dvlab/cl3d

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CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection

https://github.com/AcEbt/CPL

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Deep Insights into Noisy Pseudo Labeling on Graph Data

https://github.com/AlibabaResearch/efficientteacher

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Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

https://github.com/BIT-DA/SSAN

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Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

https://github.com/CVMI-Lab/ST3D

공식 코드

ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

https://github.com/Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning

공식 코드

Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

https://github.com/Chaolei98/FreeZAD

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Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

https://github.com/ChengJianH/CDC

공식 코드

Towards Calibrated Deep Clustering Network

https://github.com/Claydon-Wang/OFTTA

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Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition

https://github.com/Cory-M/DCCM

공식 코드

Deep Comprehensive Correlation Mining for Image Clustering

https://github.com/DZhaoXd/SND

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Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation

https://github.com/DesertsP/SLRNet

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Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

https://github.com/DongHande/PT_propagation_then_training

공식 코드

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

https://github.com/EnriqueSolarte/360-mlc

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360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning

https://github.com/Externalhappy/DPA

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DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models

https://github.com/FlyingRoastDuck/MetaCam_DSCE

공식 코드

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification

https://github.com/FujiiKazuma/CDFIAPLSUP

공식 코드

Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification

https://github.com/HHU-MMBS/plp-official-tmlr2024

공식 코드

Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection

https://github.com/HenryPengZou/JointMatch

공식 코드

JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification

https://github.com/Henryjiepanli/Building-Guided-Pseudo-Label-Learning-for-Cross-Modal-Building-Damage-Mapping

공식 코드

Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping

https://github.com/HiLab-git/SSL4MIS

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Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer

https://github.com/Hrren/Beyond-Words

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Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning

https://github.com/I2-Multimedia-Lab/A2S-v3

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Unified Unsupervised Salient Object Detection via Knowledge Transfer

https://github.com/JCZ404/Semi-DETR

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Semi-DETR: Semi-Supervised Object Detection with Detection Transformers

https://github.com/JeremyXSC/MCN-MT

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Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification

https://github.com/Jhyun17/DeYO

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Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

https://github.com/JiachengLi1995/TALLOR

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Weakly Supervised Named Entity Tagging with Learnable Logical Rules

https://github.com/JiatongXia/Pseudo-Labeling

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Enhancing Close-up Novel View Synthesis via Pseudo-labeling

https://github.com/Junjue-Wang/LoveDA

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LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

https://github.com/Junjue-Wang/LoveNAS

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LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

https://github.com/KaiyangZhou/Dassl.pytorch

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Domain Adaptive Ensemble Learning

https://github.com/KentoNishi/Augmentation-for-LNL

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Augmentation Strategies for Learning with Noisy Labels

https://github.com/KhaLee2307/StrDA

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Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition

https://github.com/KovenYu/state-information

공식 코드

Weakly supervised discriminative feature learning with state information for person identification

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