Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning. By substituting simple noising operations with advanced data augmentation methods such as RandAugment and back-translation, our method brings substantial improvements across six language and three vision tasks under the same consistency training framework. On the IMDb text classification dataset, with only 20 labeled examples, our method achieves an error rate of 4.20, outperforming the state-of-the-art model trained on 25,000 labeled examples. On a standard semi-supervised learning benchmark, CIFAR-10, our method outperforms all previous approaches and achieves an error rate of 5.43 with only 250 examples. Our method also combines well with transfer learning, e.g., when finetuning from BERT, and yields improvements in high-data regime, such as ImageNet, whether when there is only 10% labeled data or when a full labeled set with 1.3M extra unlabeled examples is used. 1

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. FixMatch first generates pseudo-labels using the model's predictions on weaklyaugmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -just 4 labels per class. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. The code is available at https://github.com/google-research/fixmatch.

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that guesses low-entropy labels for data-augmented unlabeled examples and mixes labeled and unlabeled data using MixUp. MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success. We release all code used in our experiments. 1

近年来，已取得了巨大进展，以通过半监督学习（SSL）来纳入未标记的数据来克服效率低下的监督问题。大多数最先进的模型是基于对未标记的数据追求一致的模型预测的想法，该模型被称为输入噪声，这称为一致性正则化。尽管如此，对其成功的原因缺乏理论上的见解。为了弥合理论和实际结果之间的差距，我们在本文中提出了SSL的最坏情况一致性正则化技术。具体而言，我们首先提出了针对SSL的概括，该概括由分别在标记和未标记的训练数据上观察到的经验损失项组成。在这种界限的激励下，我们得出了一个SSL目标，该目标可最大程度地减少原始未标记的样本与其多重增强变体之间最大的不一致性。然后，我们提供了一种简单但有效的算法来解决提出的最小问题，从理论上证明它会收敛到固定点。五个流行基准数据集的实验验证了我们提出的方法的有效性。

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that SSL algorithms would face in real-world applications. After creating a unified reimplementation of various widely-used SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and performance can degrade substantially when the unlabeled dataset contains out-ofdistribution examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available. 2 * Equal contribution 2 https://github.com/brain-research/realistic-ssl-evaluation 32nd Conference on Neural Information Processing Systems (NeurIPS 2018),

We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2.Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. On Im-ageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. We then train a larger Efficient-Net as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. 1 * This work was conducted at Google.

This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning (S 4 L) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that S 4 L and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels.

一个常见的分类任务情况是，有大量数据可用于培训，但只有一小部分用类标签注释。在这种情况下，半监督培训的目的是通过利用标记数据，而且从大量未标记的数据中提高分类准确性。最近的作品通过探索不同标记和未标记数据的不同增强性数据之间的一致性约束，从而取得了重大改进。遵循这条路径，我们提出了一个新颖的无监督目标，该目标侧重于彼此相似的高置信度未标记的数据之间所研究的关系较少。新提出的对损失最大程度地减少了高置信度伪伪标签之间的统计距离，其相似性高于一定阈值。我们提出的简单算法将对损失与MixMatch家族开发的技术结合在一起，显示出比以前在CIFAR-100和MINI-IMAGENET上的算法的显着性能增长，并且与CIFAR-的最先进方法相当。 10和SVHN。此外，简单还优于传输学习设置中最新方法，其中模型是由在ImainEnet或域内实现的权重初始化的。该代码可在github.com/zijian-hu/simple上获得。

一致性正则化是半监督学习（SSL）最广泛使用的技术之一。通常，目的是培训一种模型，该模型是各种数据增强的模型。在本文中，我们重新审视了这个想法，并发现通过减少来自不同增强图像之间的特征之间的距离来实现不变性，导致性能提高。然而，通过增加特征距离来鼓励其令人鼓舞，而是提高性能。为此，我们通过一个简单但有效的技术，专长的技术提出了一种改进的一致性正则化框架，它分别施加了对分类器和特征级别的一致性和增义。实验结果表明，我们的模型定义了各种数据集和设置的新技术，并以最高的余量优于以前的工作，特别是在低数据制度中。进行了广泛的实验以分析该方法，并将发布代码。

长期以来，半监督学习（SSL）已被证明是一种有限的标签模型的有效技术。在现有的文献中，基于一致性的基于正则化的方法，这些方法迫使扰动样本具有类似的预测，而原始的样本则引起了极大的关注。但是，我们观察到，当标签变得极为有限时，例如，每个类别的2或3标签时，此类方法的性能会大大降低。我们的实证研究发现，主要问题在于语义信息在数据增强过程中的漂移。当提供足够的监督时，可以缓解问题。但是，如果几乎没有指导，错误的正则化将误导网络并破坏算法的性能。为了解决该问题，我们（1）提出了一种基于插值的方法来构建更可靠的正样品对； （2）设计一种新颖的对比损失，以指导学习网络的嵌入以在样品之间进行线性更改，从而通过扩大保证金决策边界来提高网络的歧视能力。由于未引入破坏性正则化，因此我们提出的算法的性能在很大程度上得到了改善。具体而言，所提出的算法的表现优于第二好算法（COMATT），而当CIFAR-10数据集中的每个类只有两个标签可用时，可以实现88.73％的分类精度，占5.3％。此外，我们通过通过我们提出的策略大大改善现有最新算法的性能，进一步证明了所提出的方法的普遍性。

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many subpolicies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-theart. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars. * Work performed as a member of the Google Brain Residency Program.† Equal contribution.

我们研究视觉变压器（VIT）的半监督学习（SSL），尽管VIT架构广泛采用了不同的任务，但视觉变形金刚（VIT）还是一个不足的主题。为了解决这个问题，我们提出了一条新的SSL管道，该管道由第一个联合国/自制的预训练组成，然后是监督的微调，最后是半监督的微调。在半监督的微调阶段，我们采用指数的移动平均线（EMA） - 教师框架，而不是流行的FixMatch，因为前者更稳定，并且为半手不见的视觉变压器提供了更高的准确性。此外，我们提出了一种概率的伪混合机制来插入未标记的样品及其伪标签以改善正则化，这对于训练电感偏差较弱的训练VIT很重要。我们所提出的方法被称为半vit，比半监督分类设置中的CNN对应物获得可比性或更好的性能。半vit还享受VIT的可伸缩性优势，可以很容易地扩展到具有越来越高的精度的大型模型。例如，半效率总数仅使用1％标签在Imagenet上获得令人印象深刻的80％TOP-1精度，使用100％ImageNet标签与Inception-V4相当。

半监督学习（SSL）是规避建立高性能模型的昂贵标签成本的最有前途的范例之一。大多数现有的SSL方法常规假定标记和未标记的数据是从相同（类）分布中绘制的。但是，在实践中，未标记的数据可能包括课外样本；那些不能从标签数据中的封闭类中的单热编码标签，即未标记的数据是开放设置。在本文中，我们介绍了Opencos，这是一种基于最新的自我监督视觉表示学习框架来处理这种现实的半监督学习方案。具体而言，我们首先观察到，可以通过自我监督的对比度学习有效地识别开放式未标记数据集中的类外样本。然后，Opencos利用此信息来克服现有的最新半监督方法中的故障模式，通过利用一式旋转伪标签和软标签来为已识别的识别和外部未标记的标签数据分别。我们广泛的实验结果表明了Opencos的有效性，可以修复最新的半监督方法，适合涉及开放式无标记数据的各种情况。

半监督学习方法已成为对打击获得大量注释数据的挑战的活跃研究领域。为了提高半监督学习方法表现的目标，我们提出了一种新颖的框架，Hiematch，一种半监督方法，利用分层信息来降低标签成本并表现以及vanilla半监督学习方法。分层信息通常是具有细粒标签的粗标签（例如，啄木鸟）的粗标签（例如，啄木鸟）的现有知识（例如，柔软的啄木鸟或金朝啄木鸟）。但是，尚未探讨使用使用粗类标签来改进半监督技术的监督。在没有细粒度的标签的情况下，Himatch利用标签层次结构，并使用粗级标签作为弱监控信号。此外，Himatch是一种改进任何半熟的学习框架的通用方法，我们使用我们的结果在最近的最先进的技术Mixmatch和Fixmatch上展示了这一点。我们评估了在两个基准数据集，即CiFar-100和Nabirds上的Himatch疗效。与MixMatch相比，HOMACHACT可以在CIFAR-100上减少50％的粒度标签50％的用量，仅在前1个精度的边缘下降0.59％。代码：https://github.com/07agarg/hiermatch.

我们理论上和经验地证明，对抗性鲁棒性可以显着受益于半体验学习。从理论上讲，我们重新审视了Schmidt等人的简单高斯模型。这显示了标准和稳健分类之间的示例复杂性差距。我们证明了未标记的数据桥接这种差距：简单的半体验学习程序（自我训练）使用相同数量的达到高标准精度所需的标签实现高的强大精度。经验上，我们增强了CiFar-10，使用50万微小的图像，使用了8000万微小的图像，并使用强大的自我训练来优于最先进的鲁棒精度（i）$ \ ell_ infty $鲁棒性通过对抗培训和（ii）认证$ \ ell_2 $和$ \ ell_ \ infty $鲁棒性通过随机平滑的几个强大的攻击。在SVHN上，添加DataSet自己的额外训练集，删除的标签提供了4到10个点的增益，在使用额外标签的1点之内。

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict "what it is" by conventional True-Positive Classifier, while the low-confidence samples are employed to achieve a simpler goal -- to predict with ease "what it is not" by True-Negative Classifier. In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., CIFAR-10, CIFAR-100, SVHN, STL-10, mini-ImageNet and Tiny-ImageNet. More importantly, our method further shows superiority when the amount of labeled data is scarce, e.g., 92.23% accuracy with only 20 labeled data on CIFAR-10. Our code and model weights have been released at https://github.com/NJUyued/MutexMatch4SSL.

最近，出现了许多新的半监督学习方法。随着时间的流逝，ImageNet和类似数据集的准确性提高，尚未探索自然图像分类以外的任务的性能。大多数半监督的学习方法都依赖于精心设计的数据增强管道，该数据无法转移，用于在其他域的图像上学习。在这项工作中，我们提出了一种半监督的学习方法，该方法自动为特定数据集选择了最有效的数据增强策略。我们以FixMatch方法为基础，并通过增强元学习扩展它。在分类培训之前，在额外的培训中学习了增强，并利用双层优化，以优化增强政策并最大程度地提高准确性。我们在两个特定领域的数据集上评估我们的方法，其中包含卫星图像和手绘草图，并获得最新的结果。我们在消融中进一步研究与学习增强策略相关的不同参数，并展示了如何使用策略学习将增强功能调整到ImageNet之外的数据集中。

我们提出了Parse，这是一种新颖的半监督结构，用于学习强大的脑电图表现以进行情感识别。为了减少大量未标记数据与标记数据有限的潜在分布不匹配，Parse使用成对表示对准。首先，我们的模型执行数据增强，然后标签猜测大量原始和增强的未标记数据。然后将其锐化的标签和标记数据的凸组合锐化。最后，进行表示对准和情感分类。为了严格测试我们的模型，我们将解析与我们实施并适应脑电图学习的几种最先进的半监督方法进行了比较。我们对四个基于公共EEG的情绪识别数据集，种子，种子IV，种子V和Amigos（价和唤醒）进行这些实验。该实验表明，我们提出的框架在种子，种子-IV和Amigos（Valence）中的标记样品有限的情况下，取得了总体最佳效果，同时接近种子V和Amigos中的总体最佳结果（达到第二好） （唤醒）。分析表明，我们的成对表示对齐方式通过减少未标记数据和标记数据之间的分布比对来大大提高性能，尤其是当每类仅1个样本被标记时。

半监督的学习受到了最近的关注，因为它减轻了对大量标签数据的需求，这些数据通常很昂贵，需要专家知识并耗时收集。深度半监督分类的最新发展已经达到了前所未有的表现，而受监督和半监督学习之间的差距一直在挑战。这种绩效的改善是基于包含众多技术技巧，强大的增强技术和具有多项损失功能的昂贵优化方案。我们提出了一个新的框架，即laplacenet，以进行深度半监督分类，该分类大大降低了模型的复杂性。我们利用一种混合方法，在该方法中，通过将图表上的laplacian能量最小化来产生伪标记。然后，这些伪标签被用来迭代训练神经网络骨架。在几个基准数据集上，我们的模型优于深度半监督分类的最先进方法。此外，我们在理论上考虑了强大化对神经网络的应用，并证明使用多样采样方法对半监督学习的使用是合理的。我们通过严格的实验证明，多样采样增强方法可以改善概括并降低网络对增强的敏感性。

培训深层神经网络以识别图像识别通常需要大规模的人类注释数据。为了减少深神经溶液对标记数据的依赖，文献中已经提出了最先进的半监督方法。尽管如此，在面部表达识别领域（FER）领域，使用这种半监督方法非常罕见。在本文中，我们介绍了一项关于最近提出的在FER背景下的最先进的半监督学习方法的全面研究。我们对八种半监督学习方法进行了比较研究当使用各种标记的样品时。我们还将这些方法的性能与完全监督的培训进行了比较。我们的研究表明，当培训现有的半监督方法时，每类标记的样本只有250个标记的样品可以产生可比的性能，而在完整标记的数据集中训练的完全监督的方法。为了促进该领域的进一步研究，我们在：https：//github.com/shuvenduroy/ssl_fer上公开提供代码