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    • Learning from Pixel-Level Label Noise A NewPerspective for Semi-Supervised SemanticSegmentation
    • Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network
    • Semi-Supervised Learning by exploiting unlabeled data correlations in a dual-branch network
    • DMT Dynamic Mutual Training for Semi-Supervised Learning
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    • Semi-supevised Semantic Segmentation with High- and Low-level Consistency
    • Self-Tuning for Data-Efficient Deep Learning
    • FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence
      • FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
    • Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation A Baseline Investigation
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Muyun99
2021-07-29

FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence

# FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

# 作者:Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel

# 单位:Google Research

# 发表:NeurIPS 2020

# 摘要

# 阅读

# 论文的目的及结论

# 论文的实验

# 论文的方法

# 论文的背景

# 总结

# 论文的贡献
# 论文的不足
# 论文如何讲故事

# 参考资料

  • https://arxiv.org/abs/2001.07685
  • https://github.com/google-research/fixmatch
上次更新: 2021/11/03, 23:35:28
Self-Tuning for Data-Efficient Deep Learning
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation A Baseline Investigation

← Self-Tuning for Data-Efficient Deep Learning Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation A Baseline Investigation→

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