对比损失长期以来一直是深度度量学习的关键成分，现在由于自我监督学习的成功而正在变得越来越受欢迎。最近的研究表明，在学习代表网络时以互补的方式分解这种损失的损失：正期和熵项。虽然因此整体损失被定义为两种术语的组合，但这两个术语的余额通常隐藏在实施细节之后，并且在实践中很大程度上被忽略和次优。在这项工作中，我们将对比损失的平衡作为超参数优化问题，并提出了一种基于坐标的下降的搜索方法，可有效地找到优化评估性能的超参数。在此过程中，我们将现有的余额分析扩展到对比度边缘损失，包括批次大小在余额中，并解释如何从批处理中汇总损耗元素，以在更大范围内保持近最佳性能。来自深度度量学习和自我监督学习的基准的广泛实验表明，使用我们的方法比其他常用搜索方法更快地找到最佳超参数。

使用嘈杂的标签学习是一种用于图像分类的活跃研究区域。然而，研究了噪声标签对图像检索的影响。在这项工作中，我们提出了一种抗噪声的图像检索，名为基于教师的相互作用，T-SINT，它识别噪声交互，即。距离矩阵中的元素，通过使用基于教师的训练设置，在检索损失中选择正确的正面和负相互作用，这些培训设置有助于稳定性。结果，它始终如一地优于具有合成噪声和更现实的噪声的基准数据集的高噪声速率的最先进的方法。

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 .

这项工作旨在改善具有自我监督的实例检索。我们发现使用最近开发的自我监督（SSL）学习方法（如SIMCLR和MOCO）的微调未能提高实例检索的性能。在这项工作中，我们确定了例如检索的学习表示应该是不变的视点和背景等的大变化，而当前SSL方法应用的自增强阳性不能为学习强大的实例级别表示提供强大的信号。为了克服这个问题，我们提出了一种在\ texit {实例级别}对比度上建立的新SSL方法，以通过动态挖掘迷你批次和存储库来学习类内不变性训练。广泛的实验表明，insclr在实例检索上实现了比最先进的SSL方法更类似或更好的性能。代码可在https://github.com/zeludeng/insclr获得。

降低降低方法是无监督的方法，它学习了低维空间，在这些方法中，初始空间的某些特性（通常是“邻居”的概念）被保留。这种方法通常需要在大的K-NN图或复杂的优化求解器上传播。另一方面，通常用于从头开始学习表示形式，依靠简单，更可扩展的框架来学习的自我监督学习方法。在本文中，我们提出了TLDR，这是通用输入空间的一种降低方法，该方法正在移植Zbontar等人的最新自我监督学习框架。 （2021）降低维度的特定任务，超越任意表示。我们建议使用最近的邻居从训练组中构建对，并减少冗余损失，以学习在此类对之间产生表示形式的编码器。 TLDR是一种简单，易于训练和广泛适用性的方法。它由一个离线最近的邻居计算步骤组成，该步骤可以高度近似，并且是一个直接的学习过程。为了提高可伸缩性，我们专注于提高线性维度的降低，并在图像和文档检索任务上显示一致的收益，例如在Roxford上获得PCA的 +4％地图，用于GEM-AP，改善了ImageNet上的Dino的性能或以10倍的压缩保留。

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

Contrastive learning has become a key component of self-supervised learning approaches for computer vision. By learning to embed two augmented versions of the same image close to each other and to push the embeddings of different images apart, one can train highly transferable visual representations. As revealed by recent studies, heavy data augmentation and large sets of negatives are both crucial in learning such representations. At the same time, data mixing strategies, either at the image or the feature level, improve both supervised and semi-supervised learning by synthesizing novel examples, forcing networks to learn more robust features. In this paper, we argue that an important aspect of contrastive learning, i.e. the effect of hard negatives, has so far been neglected. To get more meaningful negative samples, current top contrastive self-supervised learning approaches either substantially increase the batch sizes, or keep very large memory banks; increasing memory requirements, however, leads to diminishing returns in terms of performance. We therefore start by delving deeper into a top-performing framework and show evidence that harder negatives are needed to facilitate better and faster learning. Based on these observations, and motivated by the success of data mixing, we propose hard negative mixing strategies at the feature level, that can be computed on-the-fly with a minimal computational overhead. We exhaustively ablate our approach on linear classification, object detection, and instance segmentation and show that employing our hard negative mixing procedure improves the quality of visual representations learned by a state-of-the-art self-supervised learning method.Project page: https://europe.naverlabs.com/mochi 34th Conference on Neural Information Processing Systems (NeurIPS 2020),

在图像检索中，标准评估度量依赖于分数排名，例如：平均精度（AP）。在本文中，我们介绍了一种稳健和可分解的平均精度（路线图）的方法，解决了对AP的深神经网络的端到端训练的两个主要挑战：非差异性和不分解性。首先，我们提出了一种新的等级函数的新可分辨性近似，这提供了AP损耗的上限并确保了鲁棒训练。其次，我们设计简单但有效的损失功能，以减少整个训练集中的AP之间的分解性差距及其平均批量近似，我们提供理论保证。在三个图像检索数据集上进行的广泛实验表明，路线图优于最近的几种AP近似方法，并突出了我们两个贡献的重要性。最后，使用用于训练的路线图，深度模型产生非常好的表现，表现出三个数据集的最先进结果。

我们考虑在给定的分类任务（例如Imagenet-1k（IN1K））上训练深神网络的问题，以便它在该任务以及其他（未来）转移任务方面擅长。这两个看似矛盾的属性在改善模型的概括的同时保持其在原始任务上的性能之间实现了权衡。接受自我监督学习训练的模型（SSL）倾向于比其受监督的转移学习更好地概括。但是，他们仍然落后于In1k上的监督模型。在本文中，我们提出了一个有监督的学习设置，以利用两全其美的方式。我们使用最近的SSL模型的两个关键组成部分丰富了普通的监督培训框架：多尺度农作物用于数据增强和使用可消耗的投影仪。我们用内存库在即时计算的类原型中代替了班级权重的最后一层。我们表明，这三个改进导致IN1K培训任务和13个转移任务之间的权衡取决于更加有利的权衡。在所有探索的配置中，我们都会挑出两种模型：T-Rex实现了转移学习的新状态，并且超过了In1k上的Dino和Paws等最佳方法，以及与高度优化的RSB--相匹配的T-Rex*在IN1K上的A1模型，同时在转移任务上表现更好。项目页面和预估计的模型：https：//europe.naverlabs.com/t-rex

我们研究了用于半监控学习（SSL）的无监督数据选择，其中可以提供大规模的未标记数据集，并且为标签采集预算小额数据子集。现有的SSL方法专注于学习一个有效地集成了来自给定小标记数据和大型未标记数据的信息的模型，而我们专注于选择正确的数据以用于SSL的注释，而无需任何标签或任务信息。直观地，要标记的实例应统称为下游任务的最大多样性和覆盖范围，并且单独具有用于SSL的最大信息传播实用程序。我们以三步数据为中心的SSL方法形式化这些概念，使稳定性和精度的纤维液改善8％的CiFar-10（标记为0.08％）和14％的Imagenet -1k（标记为0.2％）。它也是一种具有各种SSL方法的通用框架，提供一致的性能增益。我们的工作表明，在仔细选择注释数据上花费的小计算带来了大注释效率和模型性能增益，而无需改变学习管道。我们完全无监督的数据选择可以轻松扩展到其他弱监督的学习设置。

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.

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.

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.

图像检索通常以平均精度（AP）或召回@k进行评估。但是，这些指标仅限于二进制标签，并且不考虑错误的严重性。本文介绍了一种新的分层AP培训方法，用于相关图像检索（HAP-PIER）。 Happier是基于新的HAP度量，该指标利用概念层次结构来通过整合错误的重要性并更好地评估排名来完善AP。为了用HAP训练深层模型，我们仔细研究了问题的结构，并设计了平滑的下限替代物，并结合了聚类损失，以确保订购一致。在6个数据集上进行的广泛实验表明，更快乐的层次检索的最新方法明显优于最先进的方法，同时在评估细粒度排名表演时与最新方法相当。最后，我们表明更快乐地导致嵌入空间的更好组织，并防止最严重的非等级方法失败案例。我们的代码可在以下网址公开获取：https：//github.com/elias-ramzi/happier。

我们查看模型可解释性的特定方面：模型通常需要限制在大小上才能被认为是可解释的，例如，深度5的决策树比深度50中的一个更容易解释。但是，较小的模型也倾向于高偏见。这表明可解释性和准确性之间的权衡。我们提出了一种模型不可知论技术，以最大程度地减少这种权衡。我们的策略是首先学习甲骨文，这是培训数据上高度准确的概率模型。 Oracle预测的不确定性用于学习培训数据的抽样分布。然后，对使用此分布获得的数据样本进行了可解释的模型，通常会导致精确度明显更高。我们将抽样策略作为优化问题。我们的解决方案1具有以下关键的有利属性：（1）它使用固定数量的七个优化变量，而与数据的维度（2）无关，它是模型不可知的 - 因为可解释的模型和甲骨文都可能属于任意性模型家族（3）它具有模型大小的灵活概念，并且可以容纳向量大小（4）它是一个框架，使其能够从优化领域的进度中受益。我们还提出了以下有趣的观察结果：（a）通常，小型模型大小的最佳训练分布与测试分布不同； （b）即使可解释的模型和甲骨文来自高度截然不同的模型家族，也存在这种效果：我们通过使用封闭的复发单位网络作为甲骨文来提高决策树的序列分类精度，从而在文本分类任务上显示此效果。使用字符n-grams； （c）对于模型，我们的技术可用于确定给定样本量的最佳训练样本。

在过去几年中，无监督的学习取得了很大的进展，特别是通过对比的自我监督学习。用于基准测试自我监督学习的主导数据集已经想象，最近的方法正在接近通过完全监督培训实现的性能。然而，ImageNet DataSet在很大程度上是以对象为中心的，并且目前尚不清楚这些方法的广泛不同的数据集和任务，这些方法是非以对象为中心的，例如数字病理学。虽然自我监督的学习已经开始在这个领域探讨了令人鼓舞的结果，但有理由看起来更接近这个环境与自然图像和想象成的不同。在本文中，我们对组织病理学进行了对比学学习的深入分析，引脚指向对比物镜的表现如何不同，由于组织病理学数据的特征。我们提出了一些考虑因素，例如对比目标和超参数调整的观点。在大量的实验中，我们分析了组织分类的下游性能如何受到这些考虑因素的影响。结果指出了对比学习如何减少数字病理中的注释工作，但需要考虑特定的数据集特征。为了充分利用对比学习目标，需要不同的视野和超参数校准。我们的结果为实现组织病理学应用的自我监督学习的全部潜力铺平了道路。

深度学习在广泛的AI应用方面取得了有希望的结果。较大的数据集和模型一致地产生更好的性能。但是，我们一般花费更长的培训时间，以更多的计算和沟通。在本调查中，我们的目标是在模型精度和模型效率方面提供关于大规模深度学习优化的清晰草图。我们调查最常用于优化的算法，详细阐述了大批量培训中出现的泛化差距的可辩论主题，并审查了解决通信开销并减少内存足迹的SOTA策略。

Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations. While the performance gap between supervised and self-supervised has been narrowing, the time for training self-supervised deep networks remains an order of magnitude larger than its supervised counterparts, which hinders progress, imposes carbon cost, and limits societal benefits to institutions with substantial resources. Motivated by these issues, this paper investigates reducing the training time of recent self-supervised methods by various model-agnostic strategies that have not been used for this problem. In particular, we study three strategies: an extendable cyclic learning rate schedule, a matching progressive augmentation magnitude and image resolutions schedule, and a hard positive mining strategy based on augmentation difficulty. We show that all three methods combined lead up to 2.7 times speed-up in the training time of several self-supervised methods while retaining comparable performance to the standard self-supervised learning setting.

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.

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。