Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the "Update by Disagreement" strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-theart methods in the robustness of trained models.

Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called "Co-teaching" for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models. * The first two authors (Bo Han and Quanming Yao) made equal contributions. The implementation is available at https://github.com/bhanML/Co-teaching.32nd Conference on Neural Information Processing Systems (NIPS 2018),

Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning. The stateof-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels.

深度学习在许多领域取得了许多显着的成就，但数据集中有嘈杂的标签。使用嘈杂的标签方法共同教学和共同教学的最先进的学习+通过双网络之间的相互信息面对嘈杂的标签。但是，双网络始终倾向于收敛，这会削弱双网机制以抵抗嘈杂标签。在本文中，我们以端到端的方式提出了一个名为MLC的耐噪声框架。它通过不同的正则化来调整双网络，以确保机制的有效性。此外，我们根据双网络之间的协议纠正标签分布。提出的方法可以利用嘈杂的数据来提高网络的准确性，概括和鲁棒性。我们在模拟嘈杂的数据集MNIST，CIFAR-10和现实世界嘈杂的数据集服装上测试了提出的方法。1M。实验结果表明，我们的方法优于先前的最新方法。此外，我们的方法是无网络的，因此它适用于许多任务。我们的代码可以在https://github.com/jiarunliu/mlc上找到。

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art up to 90% noise ratio.

可以将监督学习视为将相关信息从输入数据中提取到特征表示形式。当监督嘈杂时，此过程变得困难，因为蒸馏信息可能无关紧要。实际上，最近的研究表明，网络可以轻松地过度贴合所有标签，包括损坏的标签，因此几乎无法概括以清洁数据集。在本文中，我们专注于使用嘈杂的标签学习的问题，并将压缩归纳偏置引入网络体系结构以减轻这种过度的问题。更确切地说，我们重新审视一个名为辍学的经典正则化及其变体嵌套辍学。辍学可以作为其功能删除机制的压缩约束，而嵌套辍学进一步学习有序的特征表示W.R.T.特征重要性。此外，具有压缩正则化的训练有素的模型与共同教学相结合，以提高性能。从理论上讲，我们在压缩正则化下对目标函数进行偏置变化分解。我们分析了单个模型和共同教学。该分解提供了三个见解：（i）表明过度合适确实是使用嘈杂标签学习的问题； （ii）通过信息瓶颈配方，它解释了为什么提出的特征压缩有助于对抗标签噪声； （iii）它通过将压缩正规化纳入共同教学而带来的性能提升提供了解释。实验表明，我们的简单方法比具有现实世界标签噪声（包括服装1M和Animal-10N）的基准测试标准的最先进方法具有可比性甚至更好的性能。我们的实施可在https://yingyichen-cyy.github.io/compressfatsfeatnoisylabels/上获得。

实际数据集中不可避免地有许多错误标记的数据。由于深度神经网络（DNNS）具有记忆标签的巨大能力，因此需要强大的训练方案来防止标签错误降低DNN的概括性能。当前的最新方法提出了一种共同训练方案，该方案使用与小损失相关的样本训练双网络。但是，实际上，培训两个网络可以同时负担计算资源。在这项研究中，我们提出了一种简单而有效的健壮培训计划，该计划仅通过培训一个网络来运行。在训练过程中，提出的方法通过从随机梯度下降优化形成的重量轨迹中抽样中间网络参数来生成时间自我启动。使用这些自我归档评估的损失总和用于识别错误标记的样品。同时，我们的方法通过将输入数据转换为各种形式，并考虑其协议以识别错误标记的样本来生成多视图预测。通过结合上述指标，我们介绍了提出的{\ it基于自动化的鲁棒训练}（SRT）方法，该方法可以用嘈杂的标签过滤样品，以减少其对训练的影响。广泛使用的公共数据集的实验表明，所提出的方法在某些类别中实现了最新的性能，而无需训练双网络。

带有嘈杂标签的训练深神经网络（DNN）实际上是具有挑战性的，因为不准确的标签严重降低了DNN的概括能力。以前的努力倾向于通过识别带有粗糙的小损失标准来减轻嘈杂标签的干扰的嘈杂数据来处理统一的denoising流中的零件或完整数据，而忽略了嘈杂样本的困难是不同的，因此是刚性和统一的。数据选择管道无法很好地解决此问题。在本文中，我们首先提出了一种称为CREMA的粗到精细的稳健学习方法，以分裂和串扰的方式处理嘈杂的数据。在粗糙水平中，干净和嘈杂的集合首先从统计意义上就可信度分开。由于实际上不可能正确对所有嘈杂样本进行分类，因此我们通过对每个样本的可信度进行建模来进一步处理它们。具体而言，对于清洁集，我们故意设计了一种基于内存的调制方案，以动态调整每个样本在训练过程中的历史可信度顺序方面的贡献，从而减轻了错误地分组为清洁集中的嘈杂样本的效果。同时，对于分类为嘈杂集的样品，提出了选择性标签更新策略，以纠正嘈杂的标签，同时减轻校正错误的问题。广泛的实验是基于不同方式的基准，包括图像分类（CIFAR，Clothing1M等）和文本识别（IMDB），具有合成或自然语义噪声，表明CREMA的优势和普遍性。

深度学习在大量大数据的帮助下取得了众多域中的显着成功。然而，由于许多真实情景中缺乏高质量标签，数据标签的质量是一个问题。由于嘈杂的标签严重降低了深度神经网络的泛化表现，从嘈杂的标签（强大的培训）学习是在现代深度学习应用中成为一项重要任务。在本调查中，我们首先从监督的学习角度描述了与标签噪声学习的问题。接下来，我们提供62项最先进的培训方法的全面审查，所有这些培训方法都按照其方法论差异分为五个群体，其次是用于评估其优越性的六种性质的系统比较。随后，我们对噪声速率估计进行深入分析，并总结了通常使用的评估方法，包括公共噪声数据集和评估度量。最后，我们提出了几个有前途的研究方向，可以作为未来研究的指导。所有内容将在https://github.com/songhwanjun/awesome-noisy-labels提供。

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

在标签噪声下训练深神网络的能力很有吸引力，因为不完美的注释数据相对便宜。最先进的方法基于半监督学习（SSL），该学习选择小损失示例为清洁，然后应用SSL技术来提高性能。但是，选择步骤主要提供一个中等大小的清洁子集，该子集可俯瞰丰富的干净样品。在这项工作中，我们提出了一个新颖的嘈杂标签学习框架Promix，试图最大程度地提高清洁样品的实用性以提高性能。我们方法的关键是，我们提出了一种匹配的高信心选择技术，该技术选择了那些具有很高置信的示例，并与给定标签进行了匹配的预测。结合小损失选择，我们的方法能够达到99.27的精度，并在检测CIFAR-10N数据集上的干净样品时召回98.22。基于如此大的清洁数据，Promix将最佳基线方法提高了CIFAR-10N的 +2.67％，而CIFAR-100N数据集则提高了 +1.61％。代码和数据可从https://github.com/justherozen/promix获得

样品选择是减轻标签噪声在鲁棒学习中的影响的有效策略。典型的策略通常应用小损失标准来识别干净的样品。但是，这些样本位于决策边界周围，通常会与嘈杂的例子纠缠在一起，这将被此标准丢弃，从而导致概括性能的严重退化。在本文中，我们提出了一种新颖的选择策略，\ textbf {s} elf- \ textbf {f} il \ textbf {t} ering（sft），它利用历史预测中嘈杂的示例的波动来过滤它们，可以过滤它们，这可以是可以过滤的。避免在边界示例中的小损失标准的选择偏置。具体来说，我们介绍了一个存储库模块，该模块存储了每个示例的历史预测，并动态更新以支持随后的学习迭代的选择。此外，为了减少SFT样本选择偏置的累积误差，我们设计了一个正规化术语来惩罚自信的输出分布。通过通过此术语增加错误分类类别的重量，损失函数在轻度条件下标记噪声是可靠的。我们对具有变化噪声类型的三个基准测试并实现了新的最先进的实验。消融研究和进一步分析验证了SFT在健壮学习中选择样本的优点。

Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms rely on sample selection strategies wherein, essentially, a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds, and it is difficult to fix or learn these thresholds. Often, these algorithms also require information such as label noise rates which are typically unavailable in practice. In this paper, we propose an adaptive sample selection strategy that relies only on batch statistics of a given mini-batch to provide robustness against label noise. The algorithm does not have any additional hyperparameters for sample selection, does not need any information on noise rates and does not need access to separate data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.

深神经网络（DNN）的记忆效果在许多最先进的标签噪声学习方法中起着枢轴作用。为了利用这一财产，通常采用早期停止训练早期优化的伎俩。目前的方法通常通过考虑整个DNN来决定早期停止点。然而，DNN可以被认为是一系列层的组成，并且发现DNN中的后一个层对标签噪声更敏感，而其前同行是非常稳健的。因此，选择整个网络的停止点可以使不同的DNN层对抗彼此影响，从而降低最终性能。在本文中，我们建议将DNN分离为不同的部位，逐步培训它们以解决这个问题。而不是早期停止，它一次列举一个整体DNN，我们最初通过用相对大量的时期优化DNN来训练前DNN层。在培训期间，我们通过使用较少数量的时期使用较少的地层来逐步培训后者DNN层，以抵消嘈杂标签的影响。我们将所提出的方法术语作为渐进式早期停止（PES）。尽管其简单性，与早期停止相比，PES可以帮助获得更有前景和稳定的结果。此外，通过将PE与现有的嘈杂标签培训相结合，我们在图像分类基准上实现了最先进的性能。

Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mistakenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our method outperforms state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seamlessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks. The code is available in https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning.

Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. Code is available at https://github.com/LiJunnan1992/DivideMix.

Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called Men-torNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on We-bVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at https://github.com/google/mentornet.

作为标签噪声，最受欢迎的分布变化之一，严重降低了深度神经网络的概括性能，具有嘈杂标签的强大训练正在成为现代深度学习中的重要任务。在本文中，我们提出了我们的框架，在子分类器（ALASCA）上创造了自适应标签平滑，该框架提供了具有理论保证和可忽略的其他计算的可靠特征提取器。首先，我们得出标签平滑（LS）会产生隐式Lipschitz正则化（LR）。此外，基于这些推导，我们将自适应LS（ALS）应用于子分类器架构上，以在中间层上的自适应LR的实际应用。我们对ALASCA进行了广泛的实验，并将其与以前的几个数据集上的噪声燃烧方法相结合，并显示我们的框架始终优于相应的基线。

在标签 - 噪声学习中，估计过渡矩阵是一个热门话题，因为矩阵在构建统计上一致的分类器中起着重要作用。传统上，从干净的标签到嘈杂的标签（即，清洁标签过渡矩阵（CLTM））已被广泛利用，以通过使用嘈杂的数据来学习干净的标签分类器。该分类器的动机主要是输出贝叶斯的最佳预测标签，在本文中，我们研究以直接建模从贝叶斯最佳标签过渡到嘈杂标签（即贝叶斯标签，贝叶斯标签，是BLTM）），并学习分类器以预测贝叶斯最佳的分类器标签。请注意，只有嘈杂的数据，它不足以估计CLTM或BLTM。但是，贝叶斯最佳标签与干净标签相比，贝叶斯最佳标签的不确定性较小，即，贝叶斯最佳标签的类后代是一热矢量，而干净标签的载体则不是。这使两个优点能够估算BLTM，即（a）一组具有理论上保证的贝叶斯最佳标签的示例可以从嘈杂的数据中收集； （b）可行的解决方案空间要小得多。通过利用优势，我们通过采用深层神经网络来估计BLTM参数，从而更好地概括和出色的分类性能。

使用嘈杂的标签学习是一场实际上有挑战性的弱势监督。在现有文献中，开放式噪声总是被认为是有毒的泛化，类似于封闭式噪音。在本文中，我们经验证明，开放式嘈杂标签可能是无毒的，甚至有利于对固有的嘈杂标签的鲁棒性。灵感来自观察，我们提出了一种简单而有效的正则化，通过将具有动态噪声标签（ODNL）引入培训的开放式样本。使用ODNL，神经网络的额外容量可以在很大程度上以不干扰来自清洁数据的学习模式的方式消耗。通过SGD噪声的镜头，我们表明我们的方法引起的噪音是随机方向，无偏向，这可能有助于模型收敛到最小的最小值，具有卓越的稳定性，并强制执行模型以产生保守预测-of-分配实例。具有各种类型噪声标签的基准数据集的广泛实验结果表明，所提出的方法不仅提高了许多现有的强大算法的性能，而且即使在标签噪声设置中也能实现分配异点检测任务的显着改进。