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。
translated by 谷歌翻译
深度神经网络的成功在很大程度上取决于大量高质量注释的数据的可用性,但是这些数据很难或昂贵。由此产生的标签可能是类别不平衡,嘈杂或人类偏见。从不完美注释的数据集中学习无偏分类模型是一项挑战,我们通常会遭受过度拟合或不足的折磨。在这项工作中,我们彻底研究了流行的软马克斯损失和基于保证金的损失,并提供了一种可行的方法来加强通过最大化最小样本余量来限制的概括误差。我们为此目的进一步得出了最佳条件,该条件指示了类原型应锚定的方式。通过理论分析的激励,我们提出了一种简单但有效的方法,即原型锚定学习(PAL),可以轻松地将其纳入各种基于学习的分类方案中以处理不完美的注释。我们通过对合成和现实世界数据集进行广泛的实验来验证PAL对班级不平衡学习和降低噪声学习的有效性。
translated by 谷歌翻译
Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains 1 .
translated by 谷歌翻译
现实世界中的数据通常遵循长尾巴的分布,其中一些多数类别占据了大多数数据,而大多数少数族裔类别都包含有限数量的样本。分类模型最小化跨凝结的努力来代表和分类尾部类别。尽管已经对学习无偏分类器的学习问题进行了充分的研究,但代表不平衡数据的方法却没有探索。在本文中,我们专注于表示不平衡数据的表示。最近,受到监督的对比学习最近在平衡数据上表现出了有希望的表现。但是,通过我们的理论分析,我们发现对于长尾数据,它未能形成常规的单纯形,这是代表学习的理想几何配置。为了纠正SCL的优化行为并进一步改善了长尾视觉识别的性能,我们提出了平衡对比度学习(BCL)的新型损失。与SCL相比,我们在BCL:类平均水平方面有两个改进,可以平衡负类的梯度贡献。课堂组合,允许所有类都出现在每个迷你批次中。提出的平衡对比度学习(BCL)方法满足形成常规单纯形的条件并有助于跨透明拷贝的优化。配备了BCL,提出的两分支框架可以获得更强的特征表示,并在诸如CIFAR-10-LT,CIFAR-100-LT,Imagenet-LT和Inaturalist2018之类的长尾基准数据集上实现竞争性能。我们的代码可在\ href {https://github.com/flamiezhu/bcl} {this url}中获得。
translated by 谷歌翻译
In this paper, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning. Our method is a simple modification to momentum SGD where we leverage an attentional mechanism to assign an individual importance weight to each gradient in the mini-batch. Unlike many existing heuristic-driven methods for tackling data imbalance, our method is grounded in {\it theoretically justified distributionally robust optimization (DRO)}, which is guaranteed to converge to a stationary point of an information-regularized DRO problem. The individual-level weight of a sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of information-regularized DRO. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. To balance between the learning of feature extraction layers and the learning of the classifier layer, we employ a two-stage method that uses SGD for pretraining followed by ABSGD for learning a robust classifier and finetuning lower layers. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.
translated by 谷歌翻译
尽管过度参数化的模型已经在许多机器学习任务上表现出成功,但与培训不同的测试分布的准确性可能会下降。这种准确性下降仍然限制了在野外应用机器学习的限制。同时,重要的加权是一种处理分配转移的传统技术,已被证明在经验和理论上对过度参数化模型的影响较小甚至没有影响。在本文中,我们提出了重要的回火来改善决策界限,并为过度参数化模型取得更好的结果。从理论上讲,我们证明在标签移位和虚假相关设置下,组温度的选择可能不同。同时,我们还证明正确选择的温度可以解脱出少数群体崩溃的分类不平衡。从经验上讲,我们使用重要性回火来实现最严重的小组分类任务的最新结果。
translated by 谷歌翻译
当训练数据集患有极端阶级失衡时,深度神经网络通常会表现不佳。最近的研究发现,以半监督的方式直接使用分布外数据(即开放式样本)培训将损害概括性能。在这项工作中,我们从理论上表明,从贝叶斯的角度来看,仍然可以利用分发数据来扩大少数群体。基于这种动机,我们提出了一种称为开放采样的新方法,该方法利用开放式嘈杂标签重新平衡培训数据集的班级先验。对于每个开放式实例,标签是​​从我们的预定义分布中取样的,该分布互补,与原始类先验的分布互补。我们从经验上表明,开放采样不仅可以重新平衡阶级先验,还鼓励神经网络学习可分离的表示。广泛的实验表明,我们提出的方法显着优于现有数据重新平衡方法,并可以提高现有最新方法的性能。
translated by 谷歌翻译
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL -- representation learning and label disambiguation -- in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning.
translated by 谷歌翻译
With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of longtailed data distribution (i.e., a few classes account for most of the data, while most classes are under-represented). Existing solutions typically adopt class re-balancing strategies such as re-sampling and re-weighting based on the number of observations for each class. In this work, we argue that as the number of samples increases, the additional benefit of a newly added data point will diminish. We introduce a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point. The effective number of samples is defined as the volume of samples and can be calculated by a simple formula (1−β n )/(1−β), where n is the number of samples and β ∈ [0, 1) is a hyperparameter. We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and iNaturalist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets. * The work was performed while Yin Cui and Yang Song worked at Google (a subsidiary of Alphabet Inc.).
translated by 谷歌翻译
不平衡的数据对基于深度学习的分类模型构成挑战。解决不平衡数据的最广泛使用的方法之一是重新加权,其中训练样本与损失功能的不同权重相关。大多数现有的重新加权方法都将示例权重视为可学习的参数,并优化了元集中的权重,因此需要昂贵的双重优化。在本文中,我们从分布的角度提出了一种基于最佳运输(OT)的新型重新加权方法。具体而言,我们将训练集视为其样品上的不平衡分布,该分布由OT运输到从元集中获得的平衡分布。训练样品的权重是分布不平衡的概率质量,并通过最大程度地减少两个分布之间的ot距离来学习。与现有方法相比,我们提出的一种方法可以脱离每次迭代时的体重学习对相关分类器的依赖性。图像,文本和点云数据集的实验表明,我们提出的重新加权方法具有出色的性能,在许多情况下实现了最新的结果,并提供了一种有希望的工具来解决不平衡的分类问题。
translated by 谷歌翻译
标签 - 不平衡和组敏感分类中的目标是优化相关的指标,例如平衡错误和相同的机会。经典方法,例如加权交叉熵,在训练深网络到训练(TPT)的终端阶段时,这是超越零训练误差的训练。这种观察发生了最近在促进少数群体更大边值的直观机制之后开发启发式替代品的动力。与之前的启发式相比,我们遵循原则性分析,说明不同的损失调整如何影响边距。首先,我们证明,对于在TPT中训练的所有线性分类器,有必要引入乘法,而不是添加性的Logit调整,以便对杂项边缘进行适当的变化。为了表明这一点,我们发现将乘法CE修改的连接到成本敏感的支持向量机。也许是违反,我们还发现,在培训开始时,相同的乘法权重实际上可以损害少数群体。因此,虽然在TPT中,添加剂调整无效,但我们表明它们可以通过对乘法重量的初始负效应进行抗衡来加速会聚。通过这些发现的动机,我们制定了矢量缩放(VS)丢失,即捕获现有技术作为特殊情况。此外,我们引入了对群体敏感分类的VS损失的自然延伸,从而以统一的方式处理两种常见类型的不平衡(标签/组)。重要的是,我们对最先进的数据集的实验与我们的理论见解完全一致,并确认了我们算法的卓越性能。最后,对于不平衡的高斯 - 混合数据,我们执行泛化分析,揭示平衡/标准错误和相同机会之间的权衡。
translated by 谷歌翻译
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild.
translated by 谷歌翻译
Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness and a probabilistic interpretation. Recently, after the success of self supervised contrastive representation learning methods, supervised contrastive methods have been proposed to learn representations and have shown superior and more robust performance, compared to solely training with cross entropy loss. However, cross entropy loss is still needed to train the final classification layer. In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss. First, we revisit a previously proposed contrastive-based objective function that approximates cross entropy loss and present a simple extension to learn the classifier jointly. Second, we propose a new version of the supervised contrastive training that learns jointly the parameters of the classifier and the backbone of the network. We empirically show that our proposed objective functions show a significant improvement over the standard cross entropy loss with more training stability and robustness in various challenging settings.
translated by 谷歌翻译
提出了一种学习算法,称为最大利润率(MM),以考虑集体不平衡数据学习问题:训练有素的模型倾向于预测大多数班级而不是少数群体。也就是说,少数群体的适合似乎是概括的挑战之一。为了对少数群体进行良好的概括,我们设计了一个新的最大利润率(MM)损失函数,通过最大程度地减少通过转移决策结合的基于利润的概括。理论上原理的标签 - 分布式利润率(LDAM)损失已成功应用于先前的策略,例如重新采样或重新采样以及有效的培训时间表。但是,他们尚未研究最大保证金损失函数。在这项研究中,我们研究了两种类型的基于硬利润的决策边界的性能,其中LDAM对人为不平衡的CIFAR-10/100的培训时间表,以进行公平的比较和有效性。
translated by 谷歌翻译
现实世界数据普遍面对严重的类别 - 不平衡问题,并且展示了长尾分布,即,大多数标签与有限的情况有关。由此类数据集监督的NA \“IVE模型更愿意占主导地位标签,遇到严重的普遍化挑战并变得不佳。我们从先前的角度提出了两种新的方法,以减轻这种困境。首先,我们推导了一个以平衡为导向的数据增强命名均匀的混合物(Unimix)促进长尾情景中的混合,采用先进的混合因子和采样器,支持少数民族。第二,受贝叶斯理论的动机,我们弄清了贝叶斯偏见(北美),是由此引起的固有偏见先前的不一致,并将其补偿为对标准交叉熵损失的修改。我们进一步证明了所提出的方法理论上和经验地确保分类校准。广泛的实验验证我们的策略是否有助于更好校准的模型,以及他们的策略组合在CIFAR-LT,ImageNet-LT和Inattations 2018上实现最先进的性能。
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
深度神经网络在严重的类不平衡数据集上的表现不佳。鉴于对比度学习的有希望的表现,我们提出了重新平衡的暹罗对比度采矿(RESCOM)来应对不平衡的识别。基于数学分析和仿真结果,我们声称监督的对比学习在原始批次和暹罗批次水平上都遭受双重失衡问题,这比长尾分类学习更为严重。在本文中,在原始批处理水平上,我们引入了级别平衡的监督对比损失,以分配不同类别的自适应权重。在暹罗批次级别,我们提出了一个级别平衡的队列,该队列维持所有类的键相同。此外,我们注意到,相对于对比度逻辑的不平衡对比损失梯度可以将其分解为阳性和负面因素,易于阳性和易于负面因素将使对比度梯度消失。我们建议有监督的正面和负面对挖掘,以获取信息对的对比度计算并改善表示形式学习。最后,为了大致最大程度地提高两种观点之间的相互信息,我们提出了暹罗平衡的软性软件,并与一阶段训练的对比损失结合。广泛的实验表明,在多个长尾识别基准上,RESCON优于先前的方法。我们的代码和模型可公开可用:https://github.com/dvlab-research/rescom。
translated by 谷歌翻译
学习深层分类模型的传统智慧是专注于糟糕的示例,并忽略远离决策边界的良好分类的例子。例如,当具有交叉熵损耗的训练时,具有更高可能性的示例(即,良好的良好示例)在后传播中贡献更小的梯度。然而,我们理论上表明,这种常见的做法阻碍了代表学习,能量优化和利润率的增长。为了抵消这种缺陷,我们建议奖励具有良好的奖励奖励良好的例子,以恢复他们对学习的贡献。这种反例理论上地解决了这三个问题。我们通过直接验证理论结果或通过对不同任务的实体分类,包括图像分类,图形分类和机器翻译。此外,本文表明,由于我们的想法可以解决这三个问题,我们可以处理复杂的情景,例如不平衡的分类,检测,以及在对抗性攻击下的应用。代码可用:https://github.com/lancopku/well-classification-examples-are-underestimated。
translated by 谷歌翻译
A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.
translated by 谷歌翻译