Large-scale supervised datasets are crucial to train convolutional neural networks (CNNs) for various computer vision problems. However, obtaining a massive amount of well-labeled data is usually very expensive and time consuming. In this paper, we introduce a general framework to train CNNs with only a limited number of clean labels and millions of easily obtained noisy labels. We model the relationships between images, class labels and label noises with a probabilistic graphical model and further integrate it into an end-to-end deep learning system. To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels. Experiments on this dataset indicate that our approach can better correct the noisy labels and improves the performance of trained CNNs.

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。

标签噪声显着降低了应用中深度模型的泛化能力。有效的策略和方法，\ Texit {例如}重新加权或损失校正，旨在在训练神经网络时缓解标签噪声的负面影响。这些现有的工作通常依赖于预指定的架构并手动调整附加的超参数。在本文中，我们提出了翘曲的概率推断（WARPI），以便在元学习情景中自适应地整理分类网络的培训程序。与确定性模型相比，WARPI通过学习摊销元网络来制定为分层概率模型，这可以解决样本模糊性，因此对严格的标签噪声更加坚固。与直接生成损耗的重量值的现有近似加权功能不同，我们的元网络被学习以估计从登录和标签的输入来估计整流向量，这具有利用躺在它们中的足够信息的能力。这提供了纠正分类网络的学习过程的有效方法，证明了泛化能力的显着提高。此外，可以将整流载体建模为潜在变量并学习元网络，可以无缝地集成到分类网络的SGD优化中。我们在嘈杂的标签上评估了四个强大学习基准的Warpi，并在变体噪声类型下实现了新的最先进的。广泛的研究和分析还展示了我们模型的有效性。

Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance degradation because DNNs easily overfit to noisy labels. To overcome this problem, we propose a joint optimization framework of learning DNN parameters and estimating true labels. Our framework can correct labels during training by alternating update of network parameters and labels. We conduct experiments on the noisy CIFAR-10 datasets and the Clothing1M dataset.The results indicate that our approach significantly outperforms other state-of-the-art methods.

遥感（RS）图像的多标签分类（MLC）的准确方法的开发是RS中最重要的研究主题之一。基于深度卷积神经网络（CNNS）的方法显示了RS MLC问题的强劲性能。然而，基于CNN的方法通常需要多个陆地覆盖类标签注释的大量可靠的训练图像。收集这些数据是耗时和昂贵的。为了解决这个问题，可包括嘈杂标签的公开专题产品可用于向RS零标记成本注释RS图像。但是，多标签噪声（可能与错误且缺少标签注释相关）可以扭曲MLC算法的学习过程。标签噪声的检测和校正是具有挑战性的任务，尤其是在多标签场景中，其中每个图像可以与多于一个标签相关联。为了解决这个问题，我们提出了一种新的噪声稳健协作多标签学习（RCML）方法，以减轻CNN模型训练期间多标签噪声的不利影响。 RCML在基于三个主模块的RS图像中识别，排名和排除噪声多标签：1）差异模块; 2）组套索模块; 3）交换模块。差异模块确保两个网络了解不同的功能，同时产生相同的预测。组套索模块的任务是检测分配给多标记训练图像的潜在嘈杂的标签，而交换模块任务致力于在两个网络之间交换排名信息。与现有的方法不同，我们提出了关于噪声分布的假设，我们所提出的RCML不会在训练集中的噪声类型之前进行任何先前的假设。我们的代码在线公开提供：http：//www.noisy-labels-in-rs.org

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。

Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE.

在深度学习的生态系统中，嘈杂的标签是不可避免的，但很麻烦，因为模型可以轻松地过度拟合它们。标签噪声有许多类型，例如对称，不对称和实例依赖性噪声（IDN），而IDN是唯一取决于图像信息的类型。鉴于标签错误很大程度上是由于图像中存在的视觉类别不足或模棱两可的信息引起的，因此对图像信息的这种依赖性使IDN成为可研究标签噪声的关键类型。为了提供一种有效的技术来解决IDN，我们提出了一种称为InstanceGM的新图形建模方法，该方法结合了判别和生成模型。实例GM的主要贡献是：i）使用连续的Bernoulli分布来培训生成模型，提供了重要的培训优势，ii）探索最先进的噪声标签歧视分类器来生成清洁标签来自实例依赖性嘈杂标签样品。 InstanceGM具有当前嘈杂的学习方法的竞争力，尤其是在使用合成和现实世界数据集的IDN基准测试中，我们的方法比大多数实验中的竞争对手都表现出更好的准确性。

经过嘈杂标签训练的深层模型很容易在概括中过度拟合和挣扎。大多数现有的解决方案都是基于理想的假设，即标签噪声是类条件，即同一类的实例共享相同的噪声模型，并且独立于特征。在实践中，现实世界中的噪声模式通常更为细粒度作为实例依赖性，这构成了巨大的挑战，尤其是在阶层间失衡的情况下。在本文中，我们提出了一种两阶段的干净样品识别方法，以应对上述挑战。首先，我们采用类级特征聚类程序，以早期识别在班级预测中心附近的干净样品。值得注意的是，我们根据稀有类的预测熵来解决类不平衡问题。其次，对于接近地面真相类边界的其余清洁样品（通常与样品与实例有关的噪声混合），我们提出了一种基于一致性的新型分类方法，该方法使用两个分类器头的一致性来识别它们：一致性越高，样品清洁的可能性就越大。对几个具有挑战性的基准进行了广泛的实验，证明了我们的方法与最先进的方法相比。

Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g. confidences on labels.

深度学习的最新进展依赖于大型标签的数据集来培训大容量模型。但是，以时间和成本效益的方式收集大型数据集通常会导致标签噪声。我们提出了一种从嘈杂的标签中学习的方法，该方法利用特征空间中的训练示例之间的相似性，鼓励每个示例的预测与其最近的邻居相似。与使用多个模型或不同阶段的训练算法相比，我们的方法采用了简单，附加的正规化项的形式。它可以被解释为经典的，偏置标签传播算法的归纳版本。我们在数据集上彻底评估我们的方法评估合成（CIFAR-10，CIFAR-100）和现实（迷你网络，网络vision，Clotsing1m，Mini-Imagenet-Red）噪声，并实现竞争性或最先进的精度，在所有人之间。

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。

最近关于使用嘈杂标签的学习的研究通过利用小型干净数据集来显示出色的性能。特别是，基于模型不可知的元学习的标签校正方法进一步提高了性能，通过纠正了嘈杂的标签。但是，标签错误矫予没有保障措施，导致不可避免的性能下降。此外，每个训练步骤都需要至少三个背部传播，显着减慢训练速度。为了缓解这些问题，我们提出了一种强大而有效的方法，可以在飞行中学习标签转换矩阵。采用转换矩阵使分类器对所有校正样本持怀疑态度，这减轻了错误的错误问题。我们还介绍了一个双头架构，以便在单个反向传播中有效地估计标签转换矩阵，使得估计的矩阵紧密地遵循由标签校正引起的移位噪声分布。广泛的实验表明，我们的方法在训练效率方面表现出比现有方法相当或更好的准确性。

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.

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.

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL -- representation learning and label disambiguation -- in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.

Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes). Intuitively, CE requires an extra term to facilitate learning of hard classes, and more importantly, this term should be noise tolerant, so as to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels. We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods. We also show that SL can be easily incorporated into existing methods in order to further enhance their performance.

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。

Point cloud segmentation is a fundamental task in 3D. Despite recent progress on point cloud segmentation with the power of deep networks, current learning methods based on the clean label assumptions may fail with noisy labels. Yet, class labels are often mislabeled at both instance-level and boundary-level in real-world datasets. In this work, we take the lead in solving the instance-level label noise by proposing a Point Noise-Adaptive Learning (PNAL) framework. Compared to noise-robust methods on image tasks, our framework is noise-rate blind, to cope with the spatially variant noise rate specific to point clouds. Specifically, we propose a point-wise confidence selection to obtain reliable labels from the historical predictions of each point. A cluster-wise label correction is proposed with a voting strategy to generate the best possible label by considering the neighbor correlations. To handle boundary-level label noise, we also propose a variant ``PNAL-boundary " with a progressive boundary label cleaning strategy. Extensive experiments demonstrate its effectiveness on both synthetic and real-world noisy datasets. Even with $60\%$ symmetric noise and high-level boundary noise, our framework significantly outperforms its baselines, and is comparable to the upper bound trained on completely clean data. Moreover, we cleaned the popular real-world dataset ScanNetV2 for rigorous experiment. Our code and data is available at https://github.com/pleaseconnectwifi/PNAL.

Semi-supervised learning based methods are current SOTA solutions to the noisy-label learning problem, which rely on learning an unsupervised label cleaner first to divide the training samples into a labeled set for clean data and an unlabeled set for noise data. Typically, the cleaner is obtained via fitting a mixture model to the distribution of per-sample training losses. However, the modeling procedure is \emph{class agnostic} and assumes the loss distributions of clean and noise samples are the same across different classes. Unfortunately, in practice, such an assumption does not always hold due to the varying learning difficulty of different classes, thus leading to sub-optimal label noise partition criteria. In this work, we reveal this long-ignored problem and propose a simple yet effective solution, named \textbf{C}lass \textbf{P}rototype-based label noise \textbf{C}leaner (\textbf{CPC}). Unlike previous works treating all the classes equally, CPC fully considers loss distribution heterogeneity and applies class-aware modulation to partition the clean and noise data. CPC takes advantage of loss distribution modeling and intra-class consistency regularization in feature space simultaneously and thus can better distinguish clean and noise labels. We theoretically justify the effectiveness of our method by explaining it from the Expectation-Maximization (EM) framework. Extensive experiments are conducted on the noisy-label benchmarks CIFAR-10, CIFAR-100, Clothing1M and WebVision. The results show that CPC consistently brings about performance improvement across all benchmarks. Codes and pre-trained models will be released at \url{https://github.com/hjjpku/CPC.git}.