深度度量学习（DML）了解映射，该映射到嵌入空间，其中类似数据接近并且不同的数据远远。然而，DML的传统基于代理的损失有两个问题：渐变问题并使用多个本地中心应用现实世界数据集。此外，DML性能指标也有一些问题具有稳定性和灵活性。本文提出了多代理锚（MPA）丢失和归一化折扣累积增益（NDCG @ K）度量。本研究贡献了三个以下：（1）MPA损失能够使用多代理学习现实世界数据集。（2）MPA损失提高了神经网络的培训能力，解决了梯度问题。（3）NDCG @ K度量标准鼓励对各种数据集进行全面评估。最后，我们展示了MPA损失的有效性，MPA损失在两个用于细粒度图像的数据集上实现了最高准确性。

深度度量学习（DML）模型通常需要强大的本地和全球表示，但是，DML模型培训中的本地和全球特征的有效整合是一项挑战。 DML模型通常具有特定损耗功能，包括基于成对和基于代理的损失。基于成对的损耗函数利用数据点之间丰富的语义关系，然而，在DML模型训练期间经常遭受缓慢的收敛。另一方面，基于代理的损耗功能通常会导致培训期间收敛的显着加速，而基于代理的损失通常不会完全探索数据点之间的丰富关系。在本文中，我们提出了一种新的DML方法来解决这些挑战。所提出的DML方法通过集成对基于基于代理的损耗函数来利用丰富的数据到数据关系以及快速收敛来利用混合丢失来利用混合丢失。此外，所提出的DML方法利用全局和本地功能在DML模型培训中获得丰富的表示。最后，我们还使用二阶注意功能增强，以提高准确和有效的检索。在我们的实验中，我们在四个公共基准中广泛评估了所提出的DML方法，实验结果表明，该方法在所有基准上实现了最先进的性能。

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.

深度度量学习（DML）有助于学习嵌入功能，以将语义上的数据投射到附近的嵌入空间中，并在许多应用中起着至关重要的作用，例如图像检索和面部识别。但是，DML方法的性能通常很大程度上取决于采样方法，从训练中的嵌入空间中选择有效的数据。实际上，嵌入空间中的嵌入是通过一些深层模型获得的，其中嵌入空间通常由于缺乏训练点而在贫瘠的区域中，导致所谓的“缺失嵌入”问题。此问题可能会损害样品质量，从而导致DML性能退化。在这项工作中，我们研究了如何减轻“缺失”问题以提高采样质量并实现有效的DML。为此，我们提出了一个密集锚定的采样（DAS）方案，该方案将嵌入的数据点视为“锚”，并利用锚附近的嵌入空间来密集地生成无数据点的嵌入。具体而言，我们建议用判别性特征缩放（DFS）和多个锚点利用单个锚周围的嵌入空间，并具有记忆转换转换（MTS）。通过这种方式，通过有或没有数据点的嵌入方式，我们能够提供更多的嵌入以促进采样过程，从而提高DML的性能。我们的方法毫不费力地集成到现有的DML框架中，并在没有铃铛和哨声的情况下改进了它们。在三个基准数据集上进行的广泛实验证明了我们方法的优势。

Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeping dissimilar data away from each other. To this end, many different methods are proposed in the last decade with promising results in various applications. The success of a DML algorithm greatly depends on its loss function. However, no loss function is perfect, and it deals only with some aspects of an optimal similarity embedding. Besides, the generalizability of the DML on unseen categories during the test stage is an important matter that is not considered by existing loss functions. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep feature extractor. The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses. Since the selected losses are diverse and each emphasizes different aspects of an optimal semantic embedding, our effective combining methods yield a considerable improvement over any individual loss and generalize well on unseen categories. Here, there is no limitation in choosing loss functions, and our methods can work with any set of existing ones. Besides, they can optimize each loss function as well as its weight in an end-to-end paradigm with no need to adjust any hyper-parameter. We evaluate our methods on some popular datasets from the machine vision domain in conventional Zero-Shot-Learning (ZSL) settings. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets.

We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -an anchor point x is similar to a set of positive points Y , and dissimilar to a set of negative points Z, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N -pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples -more specifically, N -1 negative examples -and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N +1)×N . We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.

大多数深度度量学习（DML）方法采用了一种策略，该策略迫使所有积极样本在嵌入空间中靠近，同时使它们远离负面样本。但是，这种策略忽略了正（负）样本的内部关系，并且通常导致过度拟合，尤其是在存在硬样品和标签错误的情况下。在这项工作中，我们提出了一个简单而有效的正则化，即列表自我验证（LSD），该化逐渐提炼模型的知识，以适应批处理中每个样本对的更合适的距离目标。LSD鼓励在正（负）样本中更平稳的嵌入和信息挖掘，以减轻过度拟合并从而改善概括。我们的LSD可以直接集成到一般的DML框架中。广泛的实验表明，LSD始终提高多个数据集上各种度量学习方法的性能。

Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works [1, 31] have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart. In this paper, we describe an algorithm for taking full advantage of the training batches in the neural network training by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. This step enables the algorithm to learn the state of the art feature embedding by optimizing a novel structured prediction objective on the lifted problem. Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning. Our experiments on the CUB-200-2011 [37], CARS196 [19], and Online Products datasets demonstrate significant improvement over existing deep feature embedding methods on all experimented embedding sizes with the GoogLeNet [33] network.

在距离度量学习网络的培训期间，典型损耗函数的最小值可以被认为是满足由训练数据施加的一组约束的“可行点”。为此，我们将距离度量学习问题重构为查找约束集的可行点，其中训练数据的嵌入向量满足所需的类内和帧间接近度。由约束集引起的可行性集被表示为仅针对训练数据的特定样本（来自每个类别的样本）强制执行接近约束的宽松可行集合。然后，通过在那些可行的组上执行交替的投影来大致解决可行点问题。这种方法引入了正则化术语，并导致最小化具有系统批量组结构的典型损失函数，其中这些批次被约束以包含来自每个类的相同样本，用于一定数量的迭代。此外，这些特定样品可以被认为是阶级代表，允许在批量构建期间有效地利用艰难的挖掘。所提出的技术应用于良好的损失，并在斯坦福在线产品，CAR196和CUB200-2011数据集进行了评估，用于图像检索和聚类。表现优于现有技术，所提出的方法一致地提高了综合损失函数的性能，没有额外的计算成本，并通过硬负面挖掘进一步提高性能。

A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper, we provide a general weighting framework for understanding recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework, which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions; (2) we show that with GPW, various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified; (3) we propose a new loss called multi-similarity loss (MS loss) under the GPW, which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally, the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE [14] and HTL [4], by a large margin, e.g., , and 80.9% → 88.0% on In-Shop Clothes Retrieval dataset

Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and informative training signals for augmentation and generalization, recent methods focus on generating synthetic samples to boost metric learning losses. However, these methods just use the deterministic and class-independent generations (e.g., simple linear interpolation), which only can cover the limited part of distribution spaces around original samples. They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations. Therefore, generated samples not only lack rich semantics within the certain class, but also might be noisy signals to disturb training. In this paper, we propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining and boost metric learning losses. Further, for most datasets that have a few samples within the class, we propose the neighbor correction to revise the inaccurate estimations, according to our correlation discovery where similar classes generally have similar variation distributions. Extensive experiments on five benchmarks show our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%. Our code is available at https://github.com/darkpromise98/IAA

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances of the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieved this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER was evaluated on four standard benchmarks, where it consistently improved performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

Human vision is able to immediately recognize novel visual categories after seeing just one or a few training examples. We describe how to add a similar capability to ConvNet classifiers by directly setting the final layer weights from novel training examples during low-shot learning. We call this process weight imprinting as it directly sets weights for a new category based on an appropriately scaled copy of the embedding layer activations for that training example. The imprinting process provides a valuable complement to training with stochastic gradient descent, as it provides immediate good classification performance and an initialization for any further fine-tuning in the future. We show how this imprinting process is related to proxy-based embeddings. However, it differs in that only a single imprinted weight vector is learned for each novel category, rather than relying on a nearest-neighbor distance to training instances as typically used with embedding methods. Our experiments show that using averaging of imprinted weights provides better generalization than using nearest-neighbor instance embeddings.

深度度量学习（DML）旨在最大程度地减少嵌入图像中成对内部/间阶层接近性违规的经验预期损失。我们将DML与有限机会限制的可行性问题联系起来。我们表明，基于代理的DML的最小化器满足了某些机会限制，并且基于代理方法的最坏情况可以通过围绕类代理的最小球的半径来表征，以覆盖相应类的整个域样本，建议每课多个代理有助于表现。为了提供可扩展的算法并利用更多代理，我们考虑了基于代理的DML实例的最小化者所隐含的机会限制，并将DML重新制定为在此类约束的交叉点中找到可行的点，从而导致问题近似解决。迭代预测。简而言之，我们反复训练基于代理的损失，并用故意选择的新样本的嵌入来重新定位代理。我们将我们的方法应用于公认的损失，并在四个流行的基准数据集上评估图像检索。优于最先进的方法，我们的方法一致地提高了应用损失的性能。代码可在以下网址找到：https：//github.com/yetigurbuz/ccp-dml

Knowledge distillation aims at transferring knowledge acquired in one model (a teacher) to another model (a student) that is typically smaller. Previous approaches can be expressed as a form of training the student to mimic output activations of individual data examples represented by the teacher. We introduce a novel approach, dubbed relational knowledge distillation (RKD), that transfers mutual relations of data examples instead. For concrete realizations of RKD, we propose distance-wise and angle-wise distillation losses that penalize structural differences in relations. Experiments conducted on different tasks show that the proposed method improves educated student models with a significant margin. In particular for metric learning, it allows students to outperform their teachers' performance, achieving the state of the arts on standard benchmark datasets.

深度指标学习旨在学习嵌入空间，即使在训练期间他们的类是看不见的，数据之间的距离反映了他们的类等价。然而，培训中可用的有限数量排除了学习嵌入空间的概括。由此激励，我们介绍了一种新的数据增强方法，该方法合成了新颖类及其嵌入向量。我们的方法可以向嵌入式模型提供丰富的语义信息，通过在原始数据中使用新类别增强培训数据来提高其泛化。我们通过学习和利用条件生成模型来实现这个想法，其中，给定类标签和噪声，产生类的随机嵌入向量。我们所提出的发电机允许损失通过增强现实和多样的类来使用更丰富的级关系，从而更好地推广了看不见的样本。公共基准数据集上的实验结果表明，我们的方法明确提高了基于代理的损失的性能。

优化平均精度（AP）的近似已被广泛研究图像检索。受AP的定义有限，这些方法考虑在每个阳性实例之前的负数和正面情况。但是，我们声称只在积极的情况下惩罚负面情况，因为损失只来自这些负面情况。为此，我们提出了一种新的损失，即惩罚正面（PNP）的负面情况，这可以直接最小化每个正面前的负实例的数量。此外，基于AP的方法采用固定和次优梯度分配策略。因此，我们通过构建损耗的衍生功能来系统地调查不同的梯度分配解决方案，导致PNP-I具有增加的衍生函数和PNP-D，其具有减小的函数。 PNP-I通过为它们分配更大的渐变并尝试使所有相关实例更近的较大渐变来重点缩影。相比之下，PNP-D对此类实例的关注不那么注意，并慢慢纠正它们。对于大多数真实世界的数据，一类通常包含几个本地群集。 PNP-我盲目地聚集了这些群集，而PNP-D保持它们。因此，PNP-D更优越。三个标准检索数据集的实验显示了上述分析的一致结果。广泛的评估表明PNP-D实现了最先进的性能。代码在https://github.com/interestingzhuo/pnp_loss获得

在本文中，我们提出了一种强大的样本生成方案来构建信息性三联网。所提出的硬样品生成是一种两级合成框架，通过两个阶段的有效正和负样品发生器产生硬样品。第一阶段将锚定向对具有分段线性操作，通过巧妙地设计条件生成的对抗网络来提高产生的样本的质量，以降低模式崩溃的风险。第二阶段利用自适应反向度量约束来生成最终的硬样本。在几个基准数据集上进行广泛的实验，验证了我们的方法比现有的硬样生成算法达到卓越的性能。此外，我们还发现，我们建议的硬样品生成方法结合现有的三态挖掘策略可以进一步提高深度度量学习性能。

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity s p and minimize the between-class similarity s n . We find a majority of loss functions, including the triplet loss and the softmax cross-entropy loss, embed s n and s p into similarity pairs and seek to reduce (s n − s p ). Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning paradigms, i.e., learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing (s n − s p ). Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several finegrained image retrieval datasets, the achieved performance is on par with the state of the art.