Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). Despite its simplicity, popularity and excellent performance, the component does not explicitly encourage discriminative learning of features. In this paper, we propose a generalized large-margin softmax (L-Softmax) loss which explicitly encourages intra-class compactness and inter-class separability between learned features. Moreover, L-Softmax not only can adjust the desired margin but also can avoid overfitting. We also show that the L-Softmax loss can be optimized by typical stochastic gradient descent. Extensive experiments on four benchmark datasets demonstrate that the deeply-learned features with L-softmax loss become more discriminative, hence significantly boosting the performance on a variety of visual classification and verification tasks.

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available 1 .

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L 2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.

In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax [10] and Angular Softmax [9] have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available 1 .

基于软马克斯的损失函数及其变体（例如，界面，圆顶和弧形）可显着改善野生无约束场景中的面部识别性能。这些算法的一种常见实践是对嵌入特征和线性转换矩阵之间的乘法进行优化。但是，在大多数情况下，基于传统的设计经验给出了嵌入功能的尺寸，并且在给出固定尺寸时，使用该功能本身提高性能的研究较少。为了应对这一挑战，本文提出了一种称为subface的软关系近似方法，该方法采用了子空间功能来促进面部识别的性能。具体而言，我们在训练过程中动态选择每个批次中的非重叠子空间特征，然后使用子空间特征在基于软磁性的损失之间近似完整功能，因此，深层模型的可区分性可以显着增强，以增强面部识别。在基准数据集上进行的综合实验表明，我们的方法可以显着提高香草CNN基线的性能，这强烈证明了基于利润率的损失的子空间策略的有效性。

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.

How to improve discriminative feature learning is central in classification. Existing works address this problem by explicitly increasing inter-class separability and intra-class similarity, whether by constructing positive and negative pairs for contrastive learning or posing tighter class separating margins. These methods do not exploit the similarity between different classes as they adhere to i.i.d. assumption in data. In this paper, we embrace the real-world data distribution setting that some classes share semantic overlaps due to their similar appearances or concepts. Regarding this hypothesis, we propose a novel regularization to improve discriminative learning. We first calibrate the estimated highest likelihood of one sample based on its semantically neighboring classes, then encourage the overall likelihood predictions to be deterministic by imposing an adaptive exponential penalty. As the gradient of the proposed method is roughly proportional to the uncertainty of the predicted likelihoods, we name it adaptive discriminative regularization (ADR), trained along with a standard cross entropy loss in classification. Extensive experiments demonstrate that it can yield consistent and non-trivial performance improvements in a variety of visual classification tasks (over 10 benchmarks). Furthermore, we find it is robust to long-tailed and noisy label data distribution. Its flexible design enables its compatibility with mainstream classification architectures and losses.

学习歧视性面部特征在建立高性能面部识别模型方面发挥着重要作用。最近的最先进的面部识别解决方案，提出了一种在常用的分类损失函数，Softmax损失中纳入固定的惩罚率，通过最大限度地减少级别的变化来增加面部识别模型的辨别力并最大化级别的帧间变化。边缘惩罚Softmax损失，如arcFace和Cosface，假设可以使用固定的惩罚余量同样地学习不同身份之间的测地距。然而，这种学习目标对于具有不一致的间帧内变化的真实数据并不是现实的，这可能限制了面部识别模型的判别和概括性。在本文中，我们通过提出弹性罚款损失（弹性面）来放松固定的罚款边缘约束，这允许在推动阶级可分离性中灵活性。主要思想是利用从每个训练迭代中的正常分布中汲取的随机保证金值。这旨在提供决策边界机会，以提取和缩回，以允许灵活的类别可分离学习的空间。我们展示了在大量主流基准上使用相同的几何变换，展示了我们的弹性面损失和COSFace损失的优势。从更广泛的角度来看，我们的弹性面在九个主流基准中提出了最先进的面部识别性能。

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

在这项工作中，我们提出了一种新的损失，以提高特征可怜和分类性能。通过自适应余弦/相干估计（ACE）的动机，我们的提出方法包括由人工神经网络本质学学习的角度信息。我们的学习ACE（蕾丝）将数据转换为新的“白细胞”空间，可提高级别的间可分离性和级别的紧凑性。我们将我们的蕾丝与基于艺术艺术品的替代最终的和功能正则化方法进行比较。我们的研究结果表明，该方法可以作为交叉熵和角度软墨水方法的可行替代方案。我们的代码是公开的：https://github.com/gatorsense/lace。

最近的工作据称，利用Softmax跨熵的分类损失不仅可以用于固定设定的分类任务，而且还通过专门为开放式任务开发的优于开销的损失，包括几次射击学习和检索。使用不同的嵌入几何形状研究了软MAX分类器 - 欧几里德，双曲线和球形，并且已经对一个或另一个的优越性进行了索赔，但它们没有得到精心控制的系统。我们对各种固定设定分类和图像检索任务的软MAX损失嵌入几何的实证研究。对于球形损失观察到的一个有趣的财产导致我们提出了一种基于VON MISES-FISHER分配的概率分类器，我们表明它具有最先进的方法竞争，同时生产出完善的盒子校准。我们提供有关亏损之间的权衡以及如何在其中选择的指导。

基于深度学习的分类中特征表示的主要挑战之一是设计表现出强大歧视力的适当损失功能。经典的SoftMax损失并不能明确鼓励对特征的歧视性学习。研究的一个流行方向是将边缘纳入良好的损失中，以实施额外的课内紧凑性和阶层间的可分离性，但是，这是通过启发式手段而不是严格的数学原则来开发的。在这项工作中，我们试图通过将原则优化目标提出为最大的利润率来解决这一限制。具体而言，我们首先将类别的边缘定义为级别间的可分离性的度量，而样品边缘是级别的紧凑性的度量。因此，为了鼓励特征的歧视性表示，损失函数应促进类和样品的最大可能边缘。此外，我们得出了广义的保证金软损失，以得出现有基于边缘的损失的一般结论。这个原则性的框架不仅提供了新的观点来理解和解释现有的基于保证金的损失，而且还提供了新的见解，可以指导新工具的设计，包括样本保证金正则化和最大的平衡案例的最大保证金损失，和零中心的正则化案例。实验结果证明了我们的策略对各种任务的有效性，包括视觉分类，分类不平衡，重新识别和面部验证。

Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax

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.

随着对人脸识别的隐私问题的增加，联邦学习被出现为研究私有分散数据的无约束人脸识别问题的最普遍的方法之一。然而，在面部识别场景中，共享客户之间的整个网络的整个参数的传统分散化联盟算法遭受了隐私泄漏。在这项工作中，我们介绍了一个框架，FEDGC，以解决联合学习，以便进行面部识别，并保证更高的隐私。我们从向后传播的角度探索校正梯度的新颖概念，并提出基于Softmax的常规程序，通过精确注入跨客户端梯度术语来校正基于SoftMax的常规程序来校正类嵌入的梯度。从理论上讲，我们表明FEDGC构成了类似于标准SoftMax的有效损失函数。已经进行了广泛的实验，以验证FEDGC的优越性，该优势可以匹配在几个流行的基准数据集上使用完整培训数据集的传统集中方法的性能。

视觉世界中新对象的不断出现对现实世界部署中当前的深度学习方法构成了巨大的挑战。由于稀有性或成本，新任务学习的挑战通常会加剧新类别的数据。在这里，我们探讨了几乎没有类别学习的重要任务（FSCIL）及其极端数据稀缺条件。理想的FSCIL模型都需要在所有类别上表现良好，无论其显示顺序或数据的匮乏。开放式现实世界条件也需要健壮，并可以轻松地适应始终在现场出现的新任务。在本文中，我们首先重新评估当前的任务设置，并为FSCIL任务提出更全面和实用的设置。然后，受到FSCIL和现代面部识别系统目标的相似性的启发，我们提出了我们的方法 - 增强角损失渐进分类或爱丽丝。在爱丽丝（Alice）中，我们建议使用角度损失损失来获得良好的特征。由于所获得的功能不仅需要紧凑，而且还需要足够多样化以维持未来的增量类别的概括，我们进一步讨论了类增强，数据增强和数据平衡如何影响分类性能。在包括CIFAR100，Miniimagenet和Cub200在内的基准数据集上的实验证明了爱丽丝在最新的FSCIL方法上的性能提高。

The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas the Cosine similarity distance is used during the testing stage for the methods maximizing margin in the angular spaces. This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time. This way, the Euclidean and Cosine distances will produce similar and consistent results and complement each other, which will in turn improve the accuracies. The proposed loss function enforces the samples of classes to cluster around the centers that represent them. The centers approximating classes are chosen from the boundary of a hypersphere, and the pairwise distances between class centers are always equivalent. This restriction corresponds to choosing centers from the vertices of a regular simplex. There is not any hyperparameter that must be set by the user in the proposed loss function, therefore the use of the proposed method is extremely easy for classical classification problems. Moreover, since the class samples are compactly clustered around their corresponding means, the proposed classifier is also very suitable for open set recognition problems where test samples can come from the unknown classes that are not seen in the training phase. Experimental studies show that the proposed method achieves the state-of-the-art accuracies on open set recognition despite its simplicity.

We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.

无线电星系的连续排放通常可以分为不同的形态学类，如FRI，Frii，弯曲或紧凑。在本文中，我们根据使用深度学习方法使用小规模数据集的深度学习方法来探讨基于形态的无线电星系分类的任务（$ \ SIM 2000 $ Samples）。我们基于双网络应用了几次射击学习技术，并使用预先培训的DENSENET模型进行了先进技术的传输学习技术，如循环学习率和歧视性学习迅速训练模型。我们使用最佳表演模型实现了超过92 \％的分类准确性，其中最大的混乱来源是弯曲和周五型星系。我们的结果表明，专注于一个小但策划数据集随着使用最佳实践来训练神经网络可能会导致良好的结果。自动分类技术对于即将到来的下一代无线电望远镜的调查至关重要，这预计将在不久的将来检测数十万个新的无线电星系。

随着最近深度卷积神经网络的进步，一般面临的概念取得了重大进展。然而，最先进的一般面部识别模型对遮挡面部图像没有概括，这正是现实世界场景中的常见情况。潜在原因是用于训练和特定设计的大规模遮挡面部数据，用于解决闭塞所带来的损坏功能。本文提出了一种新颖的面部识别方法，其基于单端到端的深神经网络的闭塞是强大的。我们的方法（使用遮挡掩码）命名（面部识别），学会发现深度卷积神经网络的损坏功能，并通过动态学习的面具清洁它们。此外，我们构建了大规模的遮挡面部图像，从有效且有效地培训。与现有方法相比，依靠外部探测器发现遮挡或采用较少鉴别的浅模型的现有方法，从简单且功能强大。 LFW，Megaface挑战1，RMF2，AR数据集和其他模拟遮挡/掩蔽数据集的实验结果证实，从大幅提高了遮挡下的准确性，并概括了一般面部识别。