【原創】【論文閱讀】2020 Learning From Noisy Large-Scale Datasets With Minimal Supervision
論文地址：https://vision.cornell.edu/se3/wp-content/uploads/2017/04/DeepLabelCleaning_CVPR.pdf 利用大規模有噪數據訓練模型的常用方法是在有噪數據上做預訓練，在精標數據上做精調。 本文提出一種利用精標數據降低有噪數據中噪聲的方法。 模型結構如下： 兩個任務： Label Cleaning Network（模型結構圖的
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相關文章
- 1. 論文筆記：Learning Denoising from Single Noisy Images
- 2. 論文閱讀：AutoAugment: Learning Augmentation Strategies from Data
- 3. 論文閱讀筆記《Boosting Few-Shot Visual Learning with Self-Supervision》
- 4. Reinforcement Learning for Relation Classification from Noisy Data閱讀筆記
- 5. 論文閱讀：Representation Learning with Contrastive Predictive Coding
- 6. 論文閱讀學習 - CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
- 7. 閱讀論文《Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs》
- 8. 【論文閱讀】NIPS2018 Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
- 9. 論文閱讀——Unsupervised Neural Machine Translation with Indirect Supervision
- 10. Learning from Noisy Anchors的思考