许多最近的自我监督学习方法在图像分类和其他任务上表现出了令人印象深刻的表现。已经使用了一种令人困惑的多种技术,并不总是清楚地了解其收益的原因,尤其是在组合使用时。在这里,我们将图像的嵌入视为点粒子,并将模型优化视为该粒子系统上的动态过程。我们的动态模型结合了类似图像的吸引力,避免局部崩溃的局部分散力以及实现颗粒的全球均匀分布的全局分散力。动态透视图突出了使用延迟参数图像嵌入(a la byol)以及同一图像的多个视图的优点。它还使用纯动态的局部分散力(布朗运动),该分散力比其他方法显示出改善的性能,并且不需要其他粒子坐标的知识。该方法称为MSBREG,代表(i)多视质心损失,它施加了吸引力的力来将不同的图像视图嵌入到其质心上,(ii)奇异值损失,将粒子系统推向空间均匀的密度( iii)布朗扩散损失。我们评估MSBREG在ImageNet上的下游分类性能以及转移学习任务,包括细粒度分类,多类对象分类,对象检测和实例分段。此外,我们还表明,将我们的正则化术语应用于其他方法,进一步改善了其性能并通过防止模式崩溃来稳定训练。
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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.
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我们提出了用于自我监督的视觉表示学习的变换不变性和协方差对比度(TICO)。与其他最新的自我监督学习方法类似,我们的方法基于同一图像的不同变形版本之间的嵌入之间的一致性,这推动了编码器产生变换不变表示。为了避免编码器生成恒定向量的微不足道解,我们通过惩罚低等级解决方案将嵌入的嵌入的协方差矩阵正常化。通过共同最大程度地减少变换不变性损失和协方差对比损失,我们得到了一个能够为下游任务产生有用表示的编码器。我们分析了我们的方法,并表明它可以被视为MOCO的变体,具有无限尺寸的隐式存储器库,无需额外的内存成本。这使我们的方法在使用小批量尺寸时的性能要比替代方法更好。 TICO也可以看作是Barlow双胞胎的修改。通过将对比度和冗余方法联系起来,TICO为我们提供了有关关节嵌入方法如何工作的新见解。
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Self-supervised learning (SSL) is rapidly closing BARLOW TWINS is competitive with state-of-the-art methods for self-supervised learning while being conceptually simpler, naturally avoiding trivial constant (i.e. collapsed) embeddings, and being robust to the training batch size.
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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.
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我们提出了一种适用于半全球任务的自学学习(SSL)方法,例如对象检测和语义分割。我们通过在训练过程中最大程度地减少像素级局部对比度(LC)损失,代表了同一图像转换版本的相应图像位置之间的局部一致性。可以将LC-LOSS添加到以最小开销的现有自我监督学习方法中。我们使用可可,Pascal VOC和CityScapes数据集评估了两个下游任务的SSL方法 - 对象检测和语义细分。我们的方法的表现优于现有的最新SSL方法可可对象检测的方法1.9%,Pascal VOC检测1.4%,而CityScapes Sementation则为0.6%。
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我们专注于更好地理解增强不变代表性学习的关键因素。我们重新访问moco v2和byol,并试图证明以下假设的真实性:不同的框架即使具有相同的借口任务也会带来不同特征的表示。我们建立了MoCo V2和BYOL之间公平比较的第一个基准,并观察:(i)复杂的模型配置使得可以更好地适应预训练数据集; (ii)从实现竞争性转移表演中获得的预训练和微调阻碍模型的优化策略不匹配。鉴于公平的基准,我们进行进一步的研究并发现网络结构的不对称性赋予对比框架在线性评估协议下正常工作,同时可能会损害长尾分类任务的转移性能。此外,负样本并不能使模型更明智地选择数据增强,也不会使不对称网络结构结构。我们相信我们的发现为将来的工作提供了有用的信息。
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We introduce Bootstrap Your Own Latent (BYOL), a new approach to selfsupervised 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. 3
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自我监督的学习表明它有可能在没有人为注释的情况下提取强大的视觉表现。提出各种作品从不同的角度处理自我监督的学习:(1)对比学习方法(例如,MOCO,SIMCLR)利用阳性和阴性样品来引导训练方向; (2)不对称网络方法(例如,BYOL,SIMSIAM)通过引入预测器网络和止动梯度操作来摆脱阴性样本; (3)特征去相关方法(例如,Barlow Twins,ViCREG),而是旨在降低特征尺寸之间的冗余。这些方法在各种动机的设计损失功能中看起来非常不同。最终的准确度数也各不相同,其中不同的网络和技巧在不同的作品中使用。在这项工作中,我们证明这些方法可以统一成相同的形式。我们不是比较他们的损失函数,我们通过梯度分析推出统一的公式。此外,我们进行公平和详细的实验以比较他们的表现。事实证明,这些方法之间几乎没有差距,并且使用动量编码器是提高性能的关键因素。从这个统一的框架来看,我们提出了一个简单但有效的自我监督学习的简单但有效的渐变形式。它不需要内存银行或预测的网络,但仍然可以实现最先进的性能,并轻松采用其他培训策略。广泛的线性评估实验和许多下游任务也表现出其有效性。代码应释放。
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对比的自我监督学习在很大程度上缩小了对想象成的预先训练的差距。然而,它的成功高度依赖于想象成的以对象形象,即相同图像的不同增强视图对应于相同的对象。当预先训练在具有许多物体的更复杂的场景图像上,如此重种策划约束会立即不可行。为了克服这一限制,我们介绍了对象级表示学习(ORL),这是一个新的自我监督的学习框架迈向场景图像。我们的主要洞察力是利用图像级自我监督的预培训作为发现对象级语义对应之前的,从而实现了从场景图像中学习的对象级表示。对Coco的广泛实验表明,ORL显着提高了自我监督学习在场景图像上的性能,甚至超过了在几个下游任务上的监督Imagenet预训练。此外,当可用更加解标的场景图像时,ORL提高了下游性能,证明其在野外利用未标记数据的巨大潜力。我们希望我们的方法可以激励未来的研究从场景数据的更多通用无人监督的代表。
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近年来,基于对比的自我监督学习方法取得了巨大的成功。但是,自学要求非常长的训练时期(例如,MoCO V3的800个时代)才能获得有希望的结果,这对于一般学术界来说是不可接受的,并阻碍了该主题的发展。这项工作重新审视了基于动量的对比学习框架,并确定了两种增强观点仅产生一个积极对的效率低下。我们提出了快速MOCO-一个新颖的框架,该框架利用组合贴片从两个增强视图中构造了多对正面,该视图提供了丰富的监督信号,这些信号带来了可忽视的额外计算成本,从而带来了显着的加速。经过100个时期训练的快速MOCO实现了73.5%的线性评估精度,类似于经过800个时期训练的MOCO V3(Resnet-50骨干)。额外的训练(200个时期)进一步将结果提高到75.1%,这与最先进的方法相当。几个下游任务的实验也证实了快速MOCO的有效性。
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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。
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数据增强模块用于对比学习将给定的数据示例转换为两个视图,这被认为是必不可少的且不可替代的。但是,多个数据增强的预定组成带来了两个缺点。首先,增强类型的人工选择为模型带来了特定的代表性不变,它们对不同的下游任务具有不同程度的积极和负面影响。在培训期间,平等处理每种类型的增强性,使该模型学习了各种下游任务的非最佳表示,并限制了事先选择增强类型的灵活性。其次,在经典的对比度学习方法中使用的强大数据增强可能会在某些情况下带来太多的不变性,而对于某些下游任务至关重要的细粒度可能会丢失。本文提出了一种通用方法,以考虑在一般的对比学习框架中考虑在何处以及与什么对比来减轻这两个问题。我们首先建议根据每个数据增强的重要性,在模型的不同深度学习不同的增强不变,而不是在骨干中均匀学习代表性不变。然后,我们建议用增强嵌入扩展对比内容,以减少强大数据增强的误导效果。基于几种基线方法的实验表明,我们在分类,检测和分割下游任务上学习更好的各种基准。
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我们通过以端到端的方式对大规模未标记的数据集进行分类,呈现扭曲,简单和理论上可解释的自我监督的表示学习方法。我们使用Softmax操作终止的暹罗网络,以产生两个增强图像的双类分布。没有监督,我们强制执行不同增强的班级分布。但是,只需最小化增强之间的分歧将导致折叠解决方案,即,输出所有图像的相同类概率分布。在这种情况下,留下有关输入图像的信息。为了解决这个问题,我们建议最大化输入和课程预测之间的互信息。具体地,我们最小化每个样品的分布的熵,使每个样品的课程预测是对每个样品自信的预测,并最大化平均分布的熵,以使不同样品的预测变得不同。以这种方式,扭曲可以自然地避免没有特定设计的折叠解决方案,例如非对称网络,停止梯度操作或动量编码器。因此,扭曲优于各种任务的最先进的方法。特别是,在半监督学习中,扭曲令人惊讶地表现出令人惊讶的是,使用Reset-50作为骨干的1%ImageNet标签实现61.2%的顶级精度,以前的最佳结果为6.2%。代码和预先训练的模型是给出的:https://github.com/byteDance/twist
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We present DetCo, a simple yet effective self-supervised approach for object detection. Unsupervised pre-training methods have been recently designed for object detection, but they are usually deficient in image classification, or the opposite. Unlike them, DetCo transfers well on downstream instance-level dense prediction tasks, while maintaining competitive image-level classification accuracy. The advantages are derived from (1) multi-level supervision to intermediate representations, (2) contrastive learning between global image and local patches. These two designs facilitate discriminative and consistent global and local representation at each level of feature pyramid, improving detection and classification, simultaneously.Extensive experiments on VOC, COCO, Cityscapes, and ImageNet demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification. For example, on ImageNet classification, DetCo is 6.9% and 5.0% top-1 accuracy better than InsLoc and DenseCL, which are two contemporary works designed for object detection. Moreover, on COCO detection, DetCo is 6.9 AP better than SwAV with Mask R-CNN C4. Notably, DetCo largely boosts up Sparse R-CNN, a recent strong detector, from 45.0 AP to 46.5 AP (+1.5 AP), establishing a new SOTA on COCO. Code is available.
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特征回归是将大型神经网络模型蒸馏到较小的功能回归。我们表明,随着网络架构的简单变化,回归可能会优于自我监督模型的知识蒸馏更复杂的最先进方法。令人惊讶的是,即使仅在蒸馏过程中仅使用并且在下游任务中丢弃时,将多层的Perceptron头部添加到CNN骨架上是有益的。因此,更深的非线性投影可以使用在不改变推理架构和时间的情况下准确地模仿老师。此外,我们利用独立的投影头来同时蒸馏多个教师网络。我们还发现,使用与教师和学生网络的输入相同的弱增强图像辅助蒸馏。Imagenet DataSet上的实验证明了各种自我监督蒸馏环境中提出的变化的功效。
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尽管增加了大量的增强家庭,但只有几个樱桃采摘的稳健增强政策有利于自我监督的图像代表学习。在本文中,我们提出了一个定向自我监督的学习范式(DSSL),其与显着的增强符号兼容。具体而言,我们在用标准增强的视图轻度增强后调整重增强策略,以产生更难的视图(HV)。 HV通常具有与原始图像较高的偏差而不是轻度增强的标准视图(SV)。与以前的方法不同,同等对称地将所有增强视图对称地最大化它们的相似性,DSSL将相同实例的增强视图视为部分有序集(具有SV $ \ LeftrightArrow $ SV,SV $ \左路$ HV),然后装备一个定向目标函数尊重视图之间的衍生关系。 DSSL可以轻松地用几行代码实现,并且对于流行的自我监督学习框架非常灵活,包括SIMCLR,Simsiam,Byol。对CiFar和Imagenet的广泛实验结果表明,DSSL可以稳定地改善各种基线,其兼容性与更广泛的增强。
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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
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In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective. However, it is unavoidable to construct undesirable views containing different semantic concepts during the augmentation procedure. It would damage the semantic consistency of representation to pull these augmentations closer in the feature space indiscriminately. In this study, we introduce feature-level augmentation and propose a novel semantics-consistent feature search (SCFS) method to mitigate this negative effect. The main idea of SCFS is to adaptively search semantics-consistent features to enhance the contrast between semantics-consistent regions in different augmentations. Thus, the trained model can learn to focus on meaningful object regions, improving the semantic representation ability. Extensive experiments conducted on different datasets and tasks demonstrate that SCFS effectively improves the performance of self-supervised learning and achieves state-of-the-art performance on different downstream tasks.
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