Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm. The resulting method is able to self-label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and ImageNet and yields the first self-supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available 1 .

Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, kmeans, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.

自我监督的视觉表示学习最近引起了重大的研究兴趣。虽然一种评估自我监督表示的常见方法是通过转移到各种下游任务，但我们研究了衡量其可解释性的问题，即了解原始表示中编码的语义。我们将后者提出为估计表示和手动标记概念空间之间的相互信息。为了量化这一点，我们介绍了一个解码瓶颈：必须通过简单的预测变量捕获信息，将概念映射到表示空间中的簇。我们称之为反向线性探测的方法为表示表示的语义敏感。该措施还能够检测出表示何时包含概念的组合（例如“红色苹果”），而不仅仅是单个属性（独立的“红色”和“苹果”）。最后，我们建议使用监督分类器自动标记大型数据集，以丰富用于探测的概念的空间。我们使用我们的方法来评估大量的自我监督表示形式，通过解释性对它们进行排名，并通过线性探针与标准评估相比出现的差异，并讨论了一些定性的见解。代码为：{\ Scriptsize {\ url {https://github.com/iro-cp/ssl-qrp}}}}}。

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or "views") of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a "swapped" prediction mechanism where we predict the code of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.

Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using non-curated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M [42], achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only non-curated raw data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.9% top-1 classification accuracy on the validation set of ImageNet, which is an improvement of +0.8% over the same network trained from scratch. Our code is available at https://github. com/facebookresearch/DeeperCluster.

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.

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 paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.

在有监督的深度学习中，学习远程感应图像（RSI）的良好表示依赖于手动注释。但是，在遥感领域，很难获得大量的标记数据。最近，自欺欺人的学习显示了其出色的学习图像表示形式的能力，尤其是实例歧视的方法。比较实例歧视的方法，基于聚类的方法不仅查看与``正面样本''相同图像的转换，而且还要查看相似的图像。在本文中，我们提出了一种基于群集的代表学习方法。我们首先介绍衡量表示表示的歧视性的数量，我们从中表明，即使分布都需要最判别的表示。这提供了理论上的见解，说明为什么均匀分发图像效果很好。我们注意到，只有保留邻里关系的均匀分布是可取的因此，我们开发了一种算法，该算法将神经网络的输出转换为实现均匀分发样品的目标，同时保留了输出的邻居关系。广泛的实验表明，我们的方法可以学习比或更好的表示形式。艺术状态的方法，我们的方法执行com在各种RSI数据集上有效地稳健地推荐。

We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches 74.3% top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and 79.6% with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub. 3 * Equal contribution; the order of first authors was randomly selected.

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that bridges contrastive learning with clustering. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it encodes semantic structures discovered by clustering into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning, which encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks with substantial improvement in low-resource transfer learning. Code and pretrained models are available at https://github.com/salesforce/PCL.

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.

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by large margins, in particular +26.6% on CI-FAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification accuracy. Furthermore, our method is the first to perform well on a large-scale dataset for image classification. In particular, we obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any groundtruth annotations. The code is made publicly available here.

对比性自我监督表示方法学习方法最大程度地提高了正对之间的相似性，同时倾向于最大程度地减少负对之间的相似性。但是，总的来说，负面对之间的相互作用被忽略了，因为它们没有根据其特定差异和相似性而采用的特殊机制来对待负面对。在本文中，我们提出了扩展的动量对比（Xmoco），这是一种基于MOCO家族配置中提出的动量编码单元的遗产，一种自我监督的表示方法。为此，我们引入了交叉一致性正则化损失，并通过该损失将转换一致性扩展到不同图像（负对）。在交叉一致性正则化规则下，我们认为与任何一对图像（正或负）相关的语义表示应在借口转换下保留其交叉相似性。此外，我们通过在批处理上的负面对上实施相似性的均匀分布来进一步规范训练损失。可以轻松地将所提出的正规化添加到现有的自我监督学习算法中。从经验上讲，我们报告了标准Imagenet-1K线性头部分类基准的竞争性能。此外，通过将学习的表示形式转移到常见的下游任务中，我们表明，将Xmoco与普遍使用的增强功能一起使用可以改善此类任务的性能。我们希望本文的发现是研究人员考虑自我监督学习中负面例子的重要相互作用的动机。

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。

近年来，已经开发了几种无监督和自我监督的方法，以从大规模未标记的数据集中学习视觉功能。然而，它们的主要缺点是，如果简单地旋转或相机的视角更改，这些方法几乎无法识别同一对象的视觉特征。为了克服此限制，同时利用有用的监督来源，我们考虑了视频对象轨道。遵循直觉，轨道中的两个补丁应该在学习的特征空间中具有相似的视觉表示形式，我们采用了一种无监督的基于群集的方法，并约束此类表示为同一类别，因为它们可能属于同一对象或对象零件。与先前的工作相比，不同数据集上两个下游任务的实验结果证明了我们在线深度聚类（ODCT）方法的有效性，而视频轨道一致性（ODCT）方法没有利用时间信息。此外，我们表明，与依靠昂贵和精确的轨道注释相比，利用无监督的类不知所措但嘈杂的轨道生成器的产量提高了准确性。

我们介绍了代表学习（CARL）的一致分配，通过组合来自自我监督对比学习和深层聚类的思路来学习视觉表现的无监督学习方法。通过从聚类角度来看对比学习，Carl通过学习一组一般原型来学习无监督的表示，该原型用作能量锚来强制执行给定图像的不同视图被分配给相同的原型。与与深层聚类的对比学习的当代工作不同，Carl建议以在线方式学习一组一般原型，使用梯度下降，而无需使用非可微分算法或k手段来解决群集分配问题。卡尔在许多代表性学习基准中超越了竞争对手，包括线性评估，半监督学习和转移学习。

We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC

Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans. However, unsupervised networks have long lagged behind the performance of their supervised counterparts, especially in the domain of large-scale visual recognition. Recent developments in training deep convolutional embeddings to maximize non-parametric instance separation and clustering objectives have shown promise in closing this gap. Here, we describe a method that trains an embedding function to maximize a metric of local aggregation, causing similar data instances to move together in the embedding space, while allowing dissimilar instances to separate. This aggregation metric is dynamic, allowing soft clusters of different scales to emerge. We evaluate our procedure on several large-scale visual recognition datasets, achieving state-of-the-art unsupervised transfer learning performance on object recognition in ImageNet, scene recognition in Places 205, and object detection in PASCAL VOC.

使用超越欧几里德距离的神经网络，深入的Bregman分歧测量数据点的分歧，并且能够捕获分布的发散。在本文中，我们提出了深深的布利曼对视觉表现的对比学习的分歧，我们的目标是通过基于功能Bregman分歧培训额外的网络来提高自我监督学习中使用的对比损失。与完全基于单点之间的分歧的传统对比学学习方法相比，我们的框架可以捕获分布之间的发散，这提高了学习表示的质量。我们展示了传统的对比损失和我们提出的分歧损失优于基线的结合，并且最先前的自我监督和半监督学习的大多数方法在多个分类和对象检测任务和数据集中。此外，学习的陈述在转移到其他数据集和任务时概括了良好。源代码和我们的型号可用于补充，并将通过纸张释放。