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.

深度神经网络在大规模标记的数据集的帮助下，在各种任务上取得了出色的表现。然而，这些数据集既耗时又竭尽全力来获得现实的任务。为了减轻对标记数据的需求，通过迭代分配伪标签将伪标签分配给未标记的样本，自我训练被广泛用于半监督学习中。尽管它很受欢迎，但自我训练还是不可靠的，通常会导致训练不稳定。我们的实验研究进一步表明，半监督学习的偏见既来自问题本身，也来自不适当的训练，并具有可能不正确的伪标签，这会在迭代自我训练过程中累积错误。为了减少上述偏见，我们提出了自我训练（DST）。首先，伪标签的生成和利用是由两个独立于参数的分类器头解耦，以避免直接误差积累。其次，我们估计自我训练偏差的最坏情况，其中伪标记函数在标记的样品上是准确的，但在未标记的样本上却尽可能多地犯错。然后，我们通过避免最坏的情况来优化表示形式，以提高伪标签的质量。广泛的实验证明，DST在标准的半监督学习基准数据集上的最先进方法中，平均提高了6.3％，而在13个不同任务上，FIXMATCH的平均水平为18.9％。此外，DST可以无缝地适应其他自我训练方法，并有助于稳定他们在从头开始的培训和预先训练模型的训练的情况下，在培训的情况下进行培训和平衡表现。

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

语义分割是开发医学图像诊断系统的重要任务。但是，构建注释的医疗数据集很昂贵。因此，在这种情况下，半监督方法很重要。在半监督学习中，标签的质量在模型性能中起着至关重要的作用。在这项工作中，我们提出了一种新的伪标签策略，可提高用于培训学生网络的伪标签的质量。我们遵循多阶段的半监督训练方法，该方法在标记的数据集上训练教师模型，然后使用训练有素的老师将伪标签渲染用于学生培训。通过这样做，伪标签将被更新，并且随着培训的进度更加精确。上一个和我们的方法之间的关键区别在于，我们在学生培训过程中更新教师模型。因此，在学生培训过程中，提高了伪标签的质量。我们还提出了一种简单但有效的策略，以使用动量模型来提高伪标签的质量 - 训练过程中原始模型的慢复制版本。通过应用动量模型与学生培训期间的重新渲染伪标签相结合，我们在五个数据集中平均达到了84.1％的骰子分数（即Kvarsir，CVC-ClinicdB，Etis-laribpolypdb，cvc-colondb，cvc-colondb，cvc-colondb和cvc-300）和CVC-300）只有20％的数据集用作标记数据。我们的结果超过了3％的共同实践，甚至在某些数据集中取得了完全监督的结果。我们的源代码和预培训模型可在https://github.com/sun-asterisk-research/online学习SSL上找到

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.

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.

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。

糖尿病性视网膜病变（DR）是发达国家工人衰老人群中失明的主要原因之一，这是由于糖尿病的副作用降低了视网膜的血液供应。深度神经网络已被广泛用于自动化系统中，以在眼底图像上进行DR分类。但是，这些模型需要大量带注释的图像。在医疗领域，专家的注释昂贵，乏味且耗时。结果，提供了有限数量的注释图像。本文提出了一种半监督的方法，该方法利用未标记的图像和标记的图像来训练一种检测糖尿病性视网膜病的模型。提出的方法通过自我监督的学习使用无监督的预告片，然后使用一小部分标记的图像和知识蒸馏来监督微调，以提高分类任务的性能。在Eyepacs测试和Messidor-2数据集中评估了此方法，仅使用2％的Eyepacs列车标记图像，分别使用0.94和0.89 AUC。

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

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。

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.

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。

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.

这项工作的作者提出了最近半监督的学习方法和相关作品的概述。尽管神经网络在各种应用中取得了显着的成功，但很少有强大的约束，包括需要大量标记数据。因此，半监督的学习是一种学习方案，其中稀缺标签和大量未标记的数据被用于训练模型（例如，深度神经网络）变得越来越重要。基于半监督学习的关键假设，这是多种假设，集群假设和连续性假设，工作回顾了最近的半监督学习方法。特别是，主要讨论了在半监督学习环境中使用深神网络的方法。此外，现有的作品首先是根据基本思想进行了分类并解释的，然后详细介绍了统一上述思想的整体方法。

在构建培训迷你批次时，最半监督的学习方法在样本标记的数据上。本文研究了这种常见做法是否改善了学习和方法。我们将其与替代设置进行比较，其中每个迷你批次从所有训练数据均匀地采样，标有或不统计，这大大减少了典型的低标签制度中真正标签的直接监督。然而，这种更简单的设置也可以看作更通用，甚至是必要的，在多任务问题中，标记数据的过采样将变得棘手。我们对半监控的CiFar-10图像分类的实验，使用FixMatch显示使用均匀采样方法时的性能下降，当标记数据的量或训练时间增加时，在均匀采样方法增加时。此外，我们分析培训动态，了解标记数据的过采样如何比较均匀采样。我们的主要发现是，在训练中特别有益，但在更多伪标签变得正确时，在后期的阶段中不太重要。尽管如此，我们还发现，保持一些真正的标签仍然很重要，以避免从错误的伪标签中积累确认错误。

现有突出物体检测模型的成功依赖于大像素标记的训练数据集。如何，收集这样的数据集不仅耗时，而且非常昂贵。为了减少标签负担，我们研究半监督的突出物体检测，并通过识别具有较小自信预测的像素来将其作为未标记的数据集像素级置信度估计问题。具体地，我们在有效的潜在空间探索之前引入了一种新的潜在变量模型，以获得有效的潜伏空间探索，导致更可靠的置信度图。通过拟议的策略，未标记的图像可以有效地参与模型培训。实验结果表明，与原始培训数据集仅有1/16的注释，与最先进的完全监督模型相比，所提出的解决方案实现了竞争性能。

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

Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, semi-supervised object detection methods, which consist in leveraging abundant unlabeled data, have been proposed and have already shown impressive results. However, most of these methods require linking a pseudo-label to a ground-truth object by thresholding. In previous works, this threshold value is usually determined empirically, which is time consuming, and only done for a single data distribution. When the domain, and thus the data distribution, changes, a new and costly parameter search is necessary. In this work, we introduce our method Adaptive Self-Training for Object Detection (ASTOD), which is a simple yet effective teacher-student method. ASTOD determines without cost a threshold value based directly on the ground value of the score histogram. To improve the quality of the teacher predictions, we also propose a novel pseudo-labeling procedure. We use different views of the unlabeled images during the pseudo-labeling step to reduce the number of missed predictions and thus obtain better candidate labels. Our teacher and our student are trained separately, and our method can be used in an iterative fashion by replacing the teacher by the student. On the MS-COCO dataset, our method consistently performs favorably against state-of-the-art methods that do not require a threshold parameter, and shows competitive results with methods that require a parameter sweep search. Additional experiments with respect to a supervised baseline on the DIOR dataset containing satellite images lead to similar conclusions, and prove that it is possible to adapt the score threshold automatically in self-training, regardless of the data distribution.

我们研究了视觉变压器的培训，用于半监督图像分类。变形金刚最近在众多监督的学习任务中表现出令人印象深刻的表现。令人惊讶的是，我们发现视觉变形金刚在半监督的想象中心设置上表现不佳。相比之下，卷积神经网络（CNNS）实现了小标记数据制度的卓越结果。进一步调查揭示了原因是CNN具有强大的空间归纳偏差。灵感来自这一观察，我们介绍了一个联合半监督学习框架，半统一，其中包含变压器分支，卷积分支和精心设计的融合模块，用于分支之间的知识共享。卷积分支在有限监督数据上培训，并生成伪标签，以监督变压器分支对未标记数据的培训。关于Imagenet的广泛实验表明，半统一达到75.5 \％的前1个精度，优于最先进的。此外，我们显示Semifirmer是一般框架，与大多数现代变压器和卷积神经结构兼容。

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。