Co-history： learning with noisy labels by co-teaching with history losses
Bibliographic record
Abstract
目的深度神经网络在计算机视觉分类任务上表现出优秀的性能，然而，在标签噪声环境下，深度学习模型面临着严峻的考验。基于协同学习（co-teaching）的学习算法能够有效缓解神经网络对噪声标签数据的学习问题，但仍然存在许多不足之处。为此，提出了一种协同学习中考虑历史信息的标签噪声鲁棒学习方法（Co-history）。方法首先，针对在噪声标签环境下使用交叉熵损失函数（cross entropy，CE）存在的过拟合问题，通过分析样本损失的历史规律，提出了修正损失函数，在模型训练时减弱CE损失带来的过拟合带来的影响。其次，针对co-teaching算法中两个网络存在过早收敛的问题，提出差异损失函数，在训练过程中保持两个网络的差异性。最后，遵循小损失选择策略，通过结合样本历史损失，提出了新的样本选择方法，可以更加精准地选择干净样本。结果在4个模拟噪声数据集F-MNIST（Fashion-mixed National Institute of Standards and Technology）、SVHN（street view house number）、CIFAR-10（Canadian Institute for Advanced Research-10）和CIFAR-100和一个真实数据集Clothing1M上进行对比实验。其中，在F-MNIST、SVHN、CIFAR-10、CIFAR-100，对称噪声（symmetric）40%噪声率下，对比co-teaching算法，本文方法分别提高了3.52%、4.77%、6.16%和6.96%；在真实数据集Clothing1M下，对比co-teaching算法，本文方法的最佳准确率和最后准确率分别提高了0.94%和1.2%。结论本文提出的协同学习下考虑历史损失的带噪声标签鲁棒分类算法，经过大量实验论证，可以有效降低噪声标签带来的影响，提高模型分类准确率。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".