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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/eliottthomas99/data_quest

공식 코드

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents

https://github.com/embar111/pgpc

공식 코드

Pseudolabel guided pixels contrast for domain adaptive semantic segmentation

https://github.com/epfl-imos/simple-sfod

공식 코드

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights

https://github.com/facebookresearch/suncet

공식 코드

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

https://github.com/fjchange/MIST_VAD

공식 코드

MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

https://github.com/fnozarian/ReliableStudent

공식 코드

Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection

https://github.com/fqhank/FuSTAL

공식 코드

Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization

https://github.com/franklinlingfeng/code-for-salcr

공식 코드

Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID

https://github.com/franklinlingfeng/code_for_mult

공식 코드

Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID

https://github.com/fy-vision/diga

공식 코드

DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation

https://github.com/gallenszl/ucfnet

공식 코드

Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching

https://github.com/gfmei/softclu

공식 코드

Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding

https://github.com/ghostish/open3dsot

공식 코드

An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-tta

공식 코드

Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation

https://github.com/google-deepmind/semppl

공식 코드

SemPPL: Predicting pseudo-labels for better contrastive representations

https://github.com/google-research/fixmatch

공식 코드

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

https://github.com/gorilla-lab-scut/ttac

공식 코드

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

https://github.com/guanghaozhu663/astmatch

공식 코드

AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation

https://github.com/gudovskiy/al-fk-self-supervision

공식 코드

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision

https://github.com/hailanyi/cpd

공식 코드

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

https://github.com/hailanyi/virconv

공식 코드

Virtual Sparse Convolution for Multimodal 3D Object Detection

https://github.com/hamperdredes/sood

공식 코드

SOOD: Towards Semi-Supervised Oriented Object Detection

https://github.com/haoxuanli-thu/dbf-unet

공식 코드

DBF-UNet: A Two-Stage Framework for Carotid Artery Segmentation with Pseudo-Label Generation

https://github.com/haru-zt/ddpls

공식 코드

Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection

https://github.com/hazel-heejeong-nam/self-supervised-causal-disentanglement

공식 코드

SCADI: Self-supervised Causal Disentanglement in Latent Variable Models

https://github.com/hengcai-nju/3d2dct

공식 코드

3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks

https://github.com/hf618/dtbs

공식 코드

DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

https://github.com/hfut-cv/lmt-gp

공식 코드

LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement

https://github.com/hilab-git/fpl-plus

공식 코드

FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation

https://github.com/hilab-git/srpl-sfda

공식 코드

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

https://github.com/hilab-git/upl-sfda

공식 코드

UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/hitsz-hlt/st-w-scorer-absa

공식 코드

Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction

https://github.com/hku-medai/wsi-hgnn

공식 코드

Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning

https://github.com/hlz0606/sspcm

공식 코드

Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module

https://github.com/hmllmh/rstc

공식 코드

Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering

https://github.com/huanglizi/lvit

공식 코드

LViT: Language meets Vision Transformer in Medical Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.