Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a "dog" can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is viewagnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks.

Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use theoretical and empirical analysis to better understand the importance of view selection, and argue that we should reduce the mutual information (MI) between views while keeping task-relevant information intact. To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their MI. We also consider data augmentation as a way to reduce MI, and show that increasing data augmentation indeed leads to decreasing MI and improves downstream classification accuracy. As a byproduct, we achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification (73% top-1 linear readout with a ResNet-50) 1 .

尽管自我监督的学习技术通常用于通过建模多种观点来从未标记的数据中挖掘隐性知识，但尚不清楚如何在复杂且不一致的环境中执行有效的表示学习。为此，我们提出了一种方法，特别是一致性和互补网络（Coconet），该方法利用了严格的全局视图一致性和局部跨视图互补性，以维护正则化，从而从多个视图中全面学习表示形式。在全球阶段，我们认为关键知识在观点之间隐含地共享，并增强编码器以从数据中捕获此类知识可以提高学习表示表示的可区分性。因此，保留多种观点的全球一致性可确保获得常识。 Coconet通过利用基于广义切成薄片的Wasserstein距离利用有效的差异度量测量来对齐视图的概率分布。最后，在本地阶段，我们提出了一个启发式互补性因素，该因素是跨观看歧视性知识的，它指导编码者不仅要学习视图的可辨别性，而且还学习跨视图互补信息。从理论上讲，我们提供了我们提出的椰子的基于信息理论的分析。从经验上讲，为了研究我们方法的改善，我们进行了足够的实验验证，这表明椰子的表现优于最先进的自我监督方法，这证明了这种隐含的一致性和互补性可以增强正则化的能力潜在表示的可区分性。

在深度学习研究中，自学学习（SSL）引起了极大的关注，引起了计算机视觉和遥感社区的兴趣。尽管计算机视觉取得了很大的成功，但SSL在地球观测领域的大部分潜力仍然锁定。在本文中，我们对在遥感的背景下为计算机视觉的SSL概念和最新发展提供了介绍，并回顾了SSL中的概念和最新发展。此外，我们在流行的遥感数据集上提供了现代SSL算法的初步基准，从而验证了SSL在遥感中的潜力，并提供了有关数据增强的扩展研究。最后，我们确定了SSL未来研究的有希望的方向的地球观察（SSL4EO），以铺平了两个领域的富有成效的相互作用。

运动，作为视频中最明显的现象，涉及随时间的变化，对视频表示学习的发展是独一无二的。在本文中，我们提出了问题：特别是对自我监督视频表示学习的运动有多重要。为此，我们撰写了一个二重奏，用于利用对比学习政权的数据增强和特征学习的动作。具体而言，我们介绍了一种以前的对比学习（MCL）方法，其将这种二重奏视为基础。一方面，MCL大写视频中的每个帧的光流量，以在时间上和空间地样本地样本（即，横跨时间的相关帧斑块的序列）作为数据增强。另一方面，MCL进一步将卷积层的梯度图对准来自空间，时间和时空视角的光流程图，以便在特征学习中地进行地面运动信息。在R（2 + 1）D骨架上进行的广泛实验证明了我们MCL的有效性。在UCF101上，在MCL学习的表示上培训的线性分类器实现了81.91％的前1个精度，表现优于6.78％的训练预测。在动力学-400上，MCL在线方案下实现66.62％的前1个精度。代码可在https://github.com/yihengzhang-cv/mcl-motion-focused-contrastive-learning。

While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss. In this work, we extend the self-supervised batch contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. We analyze two possible versions of the supervised contrastive (SupCon) loss, identifying the best-performing formulation of the loss. On ResNet-200, we achieve top-1 accuracy of 81.4% on the Ima-geNet dataset, which is 0.8% above the best number reported for this architecture. We show consistent outperformance over cross-entropy on other datasets and two ResNet variants. The loss shows benefits for robustness to natural corruptions, and is more stable to hyperparameter settings such as optimizers and data augmentations. Our loss function is simple to implement and reference TensorFlow code is released at https://t.ly/supcon 1 .

This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive selfsupervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by Sim-CLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-ofthe-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100× fewer labels. 1

This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.

神经网络二进制通过将其权重和激活量化为1位来加速深层模型。但是，二进制神经网络（BNN）与其完整精确（FP）对应物之间仍然存在巨大的性能差距。由于早期作品中权重二进制引起的量化误差已减少，因此激活二进化成为进一步提高准确性的主要障碍。 BNN表征了独特而有趣的结构，其中二进制和潜在的fp激活存在于同一正向通行证中（\ textit {i.e。} $ \ text {binarize}（\ mathbf {a} _f {a} _f）= \ mathbf {a a} _b $） 。为了减轻从FP到二元激活的二进化操作引起的信息降解，我们在通过互信息（MI）最大化的镜头训练BNN时建立了一种新颖的对比学习框架。将MI作为指标引入，以衡量二进制和FP激活之间共享的信息，这有助于对比度学习。具体而言，通过从相同输入样品中拉出二进制和FP激活的正对，以及从不同样品中推动负面对（负面对数的数量可以大大），从而极大地增强了BNN的表示能力。这使下游任务不仅有益于分类，而且还受益于分类和深度估计，〜\ textit {etc}。实验结果表明，我们的方法可以作为现有最新二元方法的堆积模块实现NYUD-V2的能力。

The objective of this paper is visual-only self-supervised video representation learning. We make the following contributions: (i) we investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (In-foNCE) training, showing that this form of supervised contrastive learning leads to a clear improvement in performance; (ii) we propose a novel self-supervised co-training scheme to improve the popular infoNCE loss, exploiting the complementary information from different views, RGB streams and optical flow, of the same data source by using one view to obtain positive class samples for the other; (iii) we thoroughly evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval. In both cases, the proposed approach demonstrates state-of-the-art or comparable performance with other self-supervised approaches, whilst being significantly more efficient to train, i.e. requiring far less training data to achieve similar performance.

自我监督的方法已通过端到端监督学习的图像分类显着缩小了差距。但是，在人类动作视频的情况下，外观和运动都是变化的重要因素，因此该差距仍然很大。这样做的关键原因之一是，采样对类似的视频剪辑，这是许多自我监督的对比学习方法所需的步骤，目前是保守的，以避免误报。一个典型的假设是，类似剪辑仅在单个视频中暂时关闭，从而导致运动相似性的示例不足。为了减轻这种情况，我们提出了SLIC，这是一种基于聚类的自我监督的对比度学习方法，用于人类动作视频。我们的关键贡献是，我们通过使用迭代聚类来分组类似的视频实例来改善传统的视频内积极采样。这使我们的方法能够利用集群分配中的伪标签来取样更艰难的阳性和负面因素。在UCF101上，SLIC的表现优于最先进的视频检索基线 +15.4％，而直接转移到HMDB51时，SLIC检索基线的率高为15.4％， +5.7％。通过用于动作分类的端到端登录，SLIC在UCF101上获得了83.2％的TOP-1准确性（+0.8％），而HMDB51（+1.6％）上的fric fineTuns in top-1 finetuning。在动力学预处理后，SLIC还与最先进的行动分类竞争。

The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations. Many pretext tasks lead to representations that are covariant with image transformations. We argue that, instead, semantic representations ought to be invariant under such transformations. Specifically, we develop Pretext-Invariant Representation Learning (PIRL, pronounced as "pearl") that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a new stateof-the-art in self-supervised learning from images on several popular benchmarks for self-supervised learning. Despite being unsupervised, PIRL outperforms supervised pre-training in learning image representations for object detection. Altogether, our results demonstrate the potential of self-supervised representations with good invariance properties.

最近，自我监督的表示学习（SSRL）在计算机视觉，语音，自然语言处理（NLP）以及最近的其他类型的模式（包括传感器的时间序列）中引起了很多关注。自我监督学习的普及是由传统模型通常需要大量通知数据进行培训的事实所驱动的。获取带注释的数据可能是一个困难且昂贵的过程。已经引入了自我监督的方法，以通过使用从原始数据自由获得的监督信号对模型进行判别预训练来提高训练数据的效率。与现有的对SSRL的评论不同，该评论旨在以单一模式为重点介绍CV或NLP领域的方法，我们旨在为时间数据提供对多模式自我监督学习方法的首次全面审查。为此，我们1）提供现有SSRL方法的全面分类，2）通过定义SSRL框架的关键组件来引入通用管道，3）根据其目标功能，网络架构和潜在应用程序，潜在的应用程序，潜在的应用程序，比较现有模型， 4）查看每个类别和各种方式中的现有多模式技术。最后，我们提出了现有的弱点和未来的机会。我们认为，我们的工作对使用多模式和/或时间数据的域中SSRL的要求有了一个观点

我们在过去十年中目睹了监督学习范式的大规模增长。监督学习需要大量标记的数据来达到最先进的性能。但是，标记样本需要很多人的注释。为避免标签数据的成本，提出了自我监督的方法来利用大部分可用的未标记数据。本研究对特征表示的自我监督范式的最新发展进行了全面和富有洞察力的调查和分析。在本文中，我们调查了影响不同环境下自我监督有用性的因素。我们展示了一些关于自我监督，生成和对比方法的两种不同方法的关键见解。我们还调查了监督对抗培训的局限性以及自我监督如何帮助克服这些限制。然后，我们继续讨论有效利用自我监督对视觉任务的局限性和挑战。最后，我们突出了一些打开的问题，并指出了未来的研究方向。

对比表示学习旨在通过估计数据的多个视图之间的共享信息来获得有用的表示形式。在这里，数据增强的选择对学会表示的质量很敏感：随着更难的应用，数据增加了，视图共享更多与任务相关的信息，但也可以妨碍表示代表的概括能力。在此激励的基础上，我们提出了一种新的强大的对比度学习计划，即r \'enyicl，可以通过利用r \'enyi差异来有效地管理更艰难的增强。我们的方法建立在r \'enyi差异的变异下限基础上，但是由于差异很大，对变异方法的使用是不切实际的。要应对这一挑战，我们提出了一个新颖的对比目标，该目标是进行变异估计的新型对比目标偏斜r \'enyi的分歧，并提供理论保证，以确保偏差差异如何导致稳定训练。我们表明，r \'enyi对比度学习目标执行先天的硬性负面样本和易于选择的阳性抽样学习有用的功能并忽略滋扰功能。通过在Imagenet上进行实验，我们表明，r \'enyi对比度学习具有更强的增强性能优于其他自我监督的方法，而无需额外的正则化或计算上的开销。图形和表格，显示了与其他对比方法相比的经验增益。

Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable. We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations. This new implementation produces features which support state-of-theart linear classification accuracy on the ImageNet dataset. When used as input for non-linear classification with deep neural networks, this representation allows us to use 2-5× less labels than classifiers trained directly on image pixels. Finally, this unsupervised representation substantially improves transfer learning to object detection on the PASCAL VOC dataset, surpassing fully supervised pre-trained ImageNet classifiers.

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning [29] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

高质量注释的医学成像数据集的稀缺性是一个主要问题，它与医学成像分析领域的机器学习应用相撞并阻碍了其进步。自我监督学习是一种最近的培训范式，可以使学习强大的表示无需人类注释，这可以被视为有效的解决方案，以解决带注释的医学数据的稀缺性。本文回顾了自我监督学习方法的最新研究方向，用于图像数据，并将其专注于其在医学成像分析领域的应用。本文涵盖了从计算机视野领域的最新自我监督学习方法，因为它们适用于医学成像分析，并将其归类为预测性，生成性和对比性方法。此外，该文章涵盖了40个在医学成像分析中自学学习领域的最新研究论文，旨在阐明该领域的最新创新。最后，本文以该领域的未来研究指示结束。

对比度学习（CL）方法有效地学习数据表示，而无需标记监督，在该方法中，编码器通过单VS-MONY SOFTMAX跨透镜损失将每个正样本在多个负样本上对比。通过利用大量未标记的图像数据，在Imagenet上预先训练时，最近的CL方法获得了有希望的结果，这是一个具有均衡图像类的曲制曲线曲线集。但是，当对野外图像进行预训练时，它们往往会产生较差的性能。在本文中，为了进一步提高CL的性能并增强其对未经保育数据集的鲁棒性，我们提出了一种双重的CL策略，该策略将其内部查询的正（负）样本对比，然后才能决定多么强烈地拉动（推）。我们通过对比度吸引力和对比度排斥（CACR）意识到这一策略，这使得查询不仅发挥了更大的力量来吸引更遥远的正样本，而且可以驱除更接近的负面样本。理论分析表明，CACR通过考虑正/阴性样品的分布之间的差异来概括CL的行为，而正/负样品的分布通常与查询独立进行采样，并且它们的真实条件分布给出了查询。我们证明了这种独特的阳性吸引力和阴性排斥机制，这有助于消除在数据集的策划较低时尤其有益于数据及其潜在表示的统一先验分布的需求。对许多标准视觉任务进行的大规模大规模实验表明，CACR不仅在表示学习中的基准数据集上始终优于现有的CL方法，而且在对不平衡图像数据集进行预训练时，还表现出更好的鲁棒性。