Co-history： learning with noisy labels by co-teaching with history losses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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。
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it