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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/xiangdal/implicit_alignment
공식 코드Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
https://github.com/xiaofeng-life/sfsnid
공식 코드https://github.com/xiaohulugo/t-ro2024-dali
공식 코드https://github.com/xiarho/madm
공식 코드Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
https://github.com/xihongyang1999/dealmvc
공식 코드DealMVC: Dual Contrastive Calibration for Multi-view Clustering
https://github.com/xihongyang1999/graphlearner
공식 코드GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
https://github.com/xmed-lab/cpr
공식 코드Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/xmed-lab/epl_semidg
공식 코드Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
https://github.com/xmuqimingxia/sp3d
공식 코드SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
https://github.com/xulianuwa/AuxSegNet
공식 코드Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
https://github.com/xushilin1/dst-det
공식 코드DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
https://github.com/yangk802/udts
공식 코드Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
https://github.com/yankai-chen/easing
공식 코드https://github.com/yanliang3612/UNREAL
공식 코드UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification
https://github.com/yao-papercodes/aglrls
공식 코드https://github.com/yaodongyu/projnorm
공식 코드Predicting Out-of-Distribution Error with the Projection Norm
https://github.com/yawencui/uad-ce
공식 코드Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
https://github.com/yaxinhou/meta-expert
공식 코드A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
https://github.com/yebo0216best/lps-main
공식 코드Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
https://github.com/yhZhai/WSCL
공식 코드Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
https://github.com/yiming-zh/DPL
공식 코드Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
https://github.com/yinghui-li-new/dapointr
공식 코드DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion
https://github.com/yinjunbo/ssda3d
공식 코드SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
https://github.com/yk-pku/two-shot-video-object-segmentation
공식 코드https://github.com/yoonkicho/pplr
공식 코드Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
https://github.com/ytaek-oh/daso
공식 코드DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning
https://github.com/yu-gi-oh-leilei/trm-ml
공식 코드Text-Region Matching for Multi-Label Image Recognition with Missing Labels
https://github.com/yuhangzang/cascadematch
공식 코드Semi-Supervised and Long-Tailed Object Detection with CascadeMatch
https://github.com/yxgeee/MMT
공식 코드https://github.com/yxgeee/SDA
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
https://github.com/yxgeee/SpCL
공식 코드Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/yxgeee/VisDA-ECCV20
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
https://github.com/yzh0905/aecl-stc
공식 코드https://github.com/yzhan238/pieclass
공식 코드https://github.com/yzou2/CBST
공식 코드Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.