Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
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Updated
May 11, 2023 - Python
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
[CVPR 2023] Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch
PyTorch implementation of consistency regularization methods for semi-supervised learning
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
90%+ with 40 labels. please see the readme for details.
A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECCV2022.
[Re-implementation] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Code for "Credal Self-Supervised Learning" as published at NeurIPS 2021.
Final project of the KTH DD2412 - Deep Learning, Advanced course.
Supplementary material and code for "Conformal Credal Self-Supervised Learning" as published at COPA 2023.
How semi-supervised learning works: FixMatch on brain-MRI-style scans, why it fails on subtle lesions, and how to fix it
Study of a powerful Semi-Supervised Learning model, FixMatch, trained on the dataset CIFAR-10
Unofficial Pytorch Implementation of 'FixMatch- Simplifying Semi-Supervised Learning with Consistency and Confidence'
SSL Methods integrated into the official implementation of the ECCV"22 Paper "CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition"
Active learning for X-ray source classification (53% label savings) plus anomaly detection and representation learning, across Chandra's three native data products.
Teaching an AI to play Geometry Dash using Reinforcement Learning and Computer Vision. Features Semi-Supervised Learning to classify game modes in real-time.
苏州科技大学《人工智能开发实训 II》(2026.05)。实践二:MSTAR SAR半监督学习(FixMatch完整网格搜索+报告分节素材);实践三:锂离子电池SOH预测(三种划分+20次独立实验+消融);ResNet32 GA+PSO通道宽度搜索(H100/L40S)。附完整工具链、实验结果与答辩素材。
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