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。

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。

我们对最近的自我和半监督ML技术进行严格的评估，从而利用未标记的数据来改善下游任务绩效，以河床分割的三个遥感任务，陆地覆盖映射和洪水映射。这些方法对于遥感任务特别有价值，因为易于访问未标记的图像，并获得地面真理标签通常可以昂贵。当未标记的图像（标记数据集之外）提供培训时，我们量化性能改进可以对这些遥感分割任务进行期望。我们还设计实验以测试这些技术的有效性，当测试集相对于训练和验证集具有域移位时。

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.

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

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.

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 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

Semi-supervised learning (SSL) has made significant strides in the field of remote sensing. Finding a large number of labeled datasets for SSL methods is uncommon, and manually labeling datasets is expensive and time-consuming. Furthermore, accurately identifying remote sensing satellite images is more complicated than it is for conventional images. Class-imbalanced datasets are another prevalent phenomenon, and models trained on these become biased towards the majority classes. This becomes a critical issue with an SSL model's subpar performance. We aim to address the issue of labeling unlabeled data and also solve the model bias problem due to imbalanced datasets while achieving better accuracy. To accomplish this, we create "artificial" labels and train a model to have reasonable accuracy. We iteratively redistribute the classes through resampling using a distribution alignment technique. We use a variety of class imbalanced satellite image datasets: EuroSAT, UCM, and WHU-RS19. On UCM balanced dataset, our method outperforms previous methods MSMatch and FixMatch by 1.21% and 0.6%, respectively. For imbalanced EuroSAT, our method outperforms MSMatch and FixMatch by 1.08% and 1%, respectively. Our approach significantly lessens the requirement for labeled data, consistently outperforms alternative approaches, and resolves the issue of model bias caused by class imbalance in datasets.

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。

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.

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.

We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.

我们提出了一个新颖的半监督学习框架，该框架巧妙地利用了模型的预测，从两个强烈的图像观点中的预测之间的一致性正则化，并由伪标签的信心加权，称为conmatch。虽然最新的半监督学习方法使用图像的弱和强烈的观点来定义方向的一致性损失，但如何为两个强大的观点之间的一致性定义定义这种方向仍然没有探索。为了解决这个问题，我们通过弱小的观点作为非参数和参数方法中的锚点来提出从强大的观点中对伪标签的新颖置信度度量。特别是，在参数方法中，我们首次介绍了伪标签在网络中的信心，该网络的信心是以端到端方式通过骨干模型学习的。此外，我们还提出了阶段训练，以提高培训的融合。当纳入现有的半监督学习者中时，并始终提高表现。我们进行实验，以证明我们对最新方法的有效性并提供广泛的消融研究。代码已在https://github.com/jiwoncocoder/conmatch上公开提供。

近年来，计算机视觉社区中最受欢迎的技术之一就是深度学习技术。作为一种数据驱动的技术，深层模型需要大量准确标记的培训数据，这在许多现实世界中通常是无法访问的。数据空间解决方案是数据增强（DA），可以人为地从原始样本中生成新图像。图像增强策略可能因数据集而有所不同，因为不同的数据类型可能需要不同的增强以促进模型培训。但是，DA策略的设计主要由具有领域知识的人类专家决定，这被认为是高度主观和错误的。为了减轻此类问题，一个新颖的方向是使用自动数据增强（AUTODA）技术自动从给定数据集中学习图像增强策略。 Autoda模型的目的是找到可以最大化模型性能提高的最佳DA策略。这项调查从图像分类的角度讨论了Autoda技术出现的根本原因。我们确定标准自动赛车模型的三个关键组件：搜索空间，搜索算法和评估功能。根据他们的架构，我们提供了现有图像AUTODA方法的系统分类法。本文介绍了Autoda领域的主要作品，讨论了他们的利弊，并提出了一些潜在的方向以进行未来的改进。

自我培训是半监督学习的有效方法。关键的想法是让学习者本身根据其当前假设而迭代地为未标记的实例生成“伪监督”。结合一致性正则化，伪标签在各个域中显示了有希望的性能，例如在计算机视觉中。为了考虑伪标签的假设性质，这些通常以概率分布的形式提供。仍然可能争辩说，即使是概率分布也代表过多的知情程度，因为它表明学习者精确地了解地面真理的条件概率。在我们的方法中，我们因此允许学习者以债务集的形式标记实例，即（候选人）概率分布。由于这种表现力增加，学习者能够以更加灵活和更忠诚的方式代表不确定性和缺乏知识。要从那种弱标记的数据中学习，我们利用最近在所谓的超集学习领域提出的方法。在详尽的经验评估中，我们将我们的方法与最先进的自我监督方法进行比较，表明竞争优越的性能，尤其是含有高度不确定性的低标签情景。

由于数据注释的高成本，半监督行动识别是一个具有挑战性的，但重要的任务是。这个问题的常见方法是用伪标签分配未标记的数据，然后将其作为训练中的额外监督。通常在最近的工作中，通过在标记数据上训练模型来获得伪标签，然后使用模型的自信预测来教授自己。在这项工作中，我们提出了一种更有效的伪标签方案，称为跨模型伪标记（CMPL）。具体地，除了主要骨干内，我们还介绍轻量级辅助网络，并要求他们互相预测伪标签。我们观察到，由于其不同的结构偏差，这两种模型倾向于学习来自同一视频剪辑的互补表示。因此，通过利用跨模型预测作为监督，每个模型都可以受益于其对应物。对不同数据分区协议的实验表明我们对现有替代方案框架的重大改进。例如，CMPL在Kinetics-400和UCF-101上实现了17.6 \％$ 17.6 \％$ 25.1 \％$ 25.使用RGB模态和1 \％$标签数据，优于我们的基线模型，FIXMATCT，以$ 9.0 \％ $和10.3美元\％$。

Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.

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),

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。