机器学习从业者通常可以访问数据的频谱：目标任务（通常是有限），未标记的数据和辅助数据的标记数据，用于其他任务的许多可用标记的数据集。我们描述了TAGLET，一个系统为学习技术，用于自动利用所有三种类型的数据并创建高质量的可服装分类器。 TAGLET的关键组件是：（1）根据知识图组织组织的辅助数据，（2）封装用于利用辅助和未标记数据的不同方法的模块，以及（3）将被整合模块组合成可用的蒸馏阶段模型。我们将TAGLETS与最先进的传输学习和半监督学习方法进行比较，四个图像分类任务。我们的研究涵盖了一系列设置，改变了标记数据的量和辅助数据的语义相关性到目标任务。我们发现，辅助和未标记数据的智能融合到多个学习技术使Taglet能够匹配 - 并且最常见的是这些替代方案。 Taglets可作为Github.com/batsresearch/taglet的开源系统使用。

大多数现有的工作在几次学习中，依赖于Meta-Learning网络在大型基础数据集上，该网络通常是与目标数据集相同的域。我们解决了跨域几秒钟的问题，其中基础和目标域之间存在大移位。与未标记的目标数据的跨域几秒识别问题在很大程度上在文献中毫无根据。启动是使用自我训练解决此问题的第一个方法。但是，它使用固定的老师在标记的基础数据集上返回，以为未标记的目标样本创建软标签。由于基本数据集和未标记的数据集来自不同的域，因此将基本数据集的类域中的目标图像投影，具有固定的预制模型可能是子最优的。我们提出了一种简单的动态蒸馏基方法，以方便来自新颖/基础数据集的未标记图像。我们通过从教师网络中的未标记图像的未标记版本的预测计算并将其与来自学生网络相同的相同图像的强大版本匹配来施加一致性正常化。教师网络的参数被更新为学生网络参数的指数移动平均值。我们表明所提出的网络了解可以轻松适应目标域的表示，即使它尚未在预先预测阶段的目标专用类别训练。我们的车型优于当前最先进的方法，在BSCD-FSL基准中的5次分类，3.6％的3.6％，并在传统的域名几枪学习任务中显示出竞争性能。

我们提出了将粗大分类标签纳入细粒域中的图像分类器的技术。这种标签通常可以通过较小的努力来获得较小的粒状域，例如根据生物分类法组织类别的自然界。在三个王国组成的半inat数据集上，包括Phylum标签，在使用ImageNet预训练模型的转移学习设置中将物种级别分类精度提高了6％。使用称为FixMatch的最先进的半监督学习算法的分层标签结构提高了1.3％的性能。当提供诸如类或订单的详细标签或从头开始培训时，相对收益更大。但是，我们发现大多数方法对来自新类别的域名数据的存在并不强大。我们提出了一种技术来从层次结构引导的大量未标记图像中选择相关数据，这提高了鲁棒性。总体而言，我们的实验表明，具有粗大分类标签的半监督学习对于细粒度域中的培训分类器是实用的。

少量分类需要调整从大型注释的基础数据集中学到的知识来识别新颖的看不见的类，每个类别由少数标记的示例表示。在这样的场景中，预先绘制大容量在大型数据集上的网络，然后在少数示例下向少量抵消导致严重的过度拟合。同时，在从大型标记数据集中学到的“冷冻”特征的顶部培训一个简单的线性分类器无法使模型调整到新型类的属性，有效地诱导底部。在本文中，我们向这两种流行的策略提出了一种替代方法。首先，我们的方法使用在新颖类上培训的线性分类器来伪标签整个大型数据集。这有效地“幻觉”在大型数据集中的新型类别，尽管基本数据库中未存在的新类别（新颖和基类是不相交的）。然后，除了在新型数据集上的标准交叉熵损失之外，它将在伪标记的基础示例上具有蒸馏损失的整个模型。这一步骤有效地训练了网络，识别对新型类别识别的上下文和外观提示，而是使用整个大规模基础数据集，从而克服了几次拍摄学习的固有数据稀缺问题。尽管这种方法的简单性，但我们表明我们的方法在四个成熟的少量分类基准上表现出最先进的。

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.

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

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.

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.

零拍的学习依赖于语义类表示，例如手工设计的属性或学习的嵌入方式来预测类，而无需任何标记的示例。我们建议通过将节点从矢量空间中的常识知识图中嵌入节点来学习班级表示。常识知识图是未开发的明确高级知识的来源，几乎不需要人类的努力才能应用于一系列任务。为了捕获图中的知识，我们引入了ZSL-KG，这是一种具有新型变压器图卷积网络（TRGCN）的通用框架，用于生成类表示。我们提出的TRGCN体系结构计算节点社区的非线性组合。我们的结果表明，ZSL-KG在语言和视觉中的六个零弹药基准数据集中有五个基于WordNet的方法改进了基于WordNet的方法。

这项工作研究了伪标签的偏见问题，一种广泛发生的自然现象，但经常通过先前的研究忽视。当在源数据上培训的分类器被传送到未标记的目标数据时，会生成伪标签。当半监督的学习模型Fixmatch预测未标记的数据时，我们观察到沉重的长尾伪标签即使未标记的数据被策划到平衡。没有干预，培训模型继承了伪标签的偏置，最终是次优。为了消除模型偏置，我们提出了一种简单而有效的方法DebiSmatch，包括自适应脱叠模块和自适应边际损失。通过使用在线更新的队列，可以自动调整脱叠的强度和边距的大小。在ImageNet-1K上基准测试，DebiasMatch分别在半监督学习（0.2％注释数据）和零拍摄学习任务中显着超过26％和8.7％的最先进。

Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -from 1 example per class to 1 M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.

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.

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.

在元学习框架下设计了许多射门学习方法，这些方法从各种学习任务中学习并推广到新任务。这些元学习方法在从同一分布（I.I.D.观察）中绘制的所有样本中的情况下实现了预期的性能。然而，在现实世界应用中，很少拍摄的学习范式往往遭受数据转移，即，即使在相同的任务中，也可以从各种数据分布中汲取不同任务中的示例。大多数现有的几次拍摄方法不考虑数据班次，因此在数据分布换档时显示降级性能。然而，由于每个任务中的标记样本数量有限的标记样本，因此在几次拍摄学习中解决数据转换问题是不普遍的。针对解决此问题，我们提出了一种新的基于度量的元学习框架，以便在知识图表的帮助下提取任务特定的表示和任务共享表示。因此，任务内的数据偏移可以通过任务共享和特定于任务的表示的组合来组合。拟议的模型是对流行的基准测试和两个构造的新具有挑战性的数据集。评估结果表明了其显着性能。

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.

我们对自我监督，监督或半监督设置的代表学习感兴趣。在应用自我监督学习的平均移位思想的事先工作，通过拉动查询图像来概括拜尔的想法，不仅更接近其其他增强，而且还可以到其他增强的最近邻居（NNS）。我们认为，学习可以从选择远处与查询相关的邻居选择遥远的邻居。因此，我们建议通过约束最近邻居的搜索空间来概括MSF算法。我们显示我们的方法在SSL设置中优于MSF，当约束使用不同的图像时，并且当约束确保NNS具有与查询相同的伪标签时，在半监控设置中优于培训资源的半监控设置中的爪子。

元学习已成为几乎没有图像分类的实用方法，在该方法中，“学习分类器的策略”是在标记的基础类别上进行元学习的，并且可以应用于具有新颖类的任务。我们删除了基类标签的要求，并通过无监督的元学习（UML）学习可通用的嵌入。具体而言，任务发作是在元训练过程中使用未标记的基本类别的数据增强构建的，并且我们将基于嵌入式的分类器应用于新的任务，并在元测试期间使用标记的少量示例。我们观察到两个元素在UML中扮演着重要角色，即进行样本任务和衡量实例之间的相似性的方法。因此，我们获得了具有两个简单修改的​​强基线 - 一个足够的采样策略，每情节有效地构建多个任务以及半分解的相似性。然后，我们利用来自两个方向的任务特征以获得进一步的改进。首先，合成的混淆实例被合并以帮助提取更多的判别嵌入。其次，我们利用额外的特定任务嵌入转换作为元训练期间的辅助组件，以促进预先适应的嵌入式的概括能力。几乎没有学习基准的实验证明，我们的方法比以前的UML方法优于先前的UML方法，并且比其监督变体获得了可比甚至更好的性能。

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

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。

语义分割在广泛的计算机视觉应用中起着基本作用，提供了全球对图像​​的理解的关键信息。然而，最先进的模型依赖于大量的注释样本，其比在诸如图像分类的任务中获得更昂贵的昂贵的样本。由于未标记的数据替代地获得更便宜，因此无监督的域适应达到了语义分割社区的广泛成功并不令人惊讶。本调查致力于总结这一令人难以置信的快速增长的领域的五年，这包含了语义细分本身的重要性，以及将分段模型适应新环境的关键需求。我们提出了最重要的语义分割方法;我们对语义分割的域适应技术提供了全面的调查;我们揭示了多域学习，域泛化，测试时间适应或无源域适应等较新的趋势;我们通过描述在语义细分研究中最广泛使用的数据集和基准测试来结束本调查。我们希望本调查将在学术界和工业中提供具有全面参考指导的研究人员，并有助于他们培养现场的新研究方向。