尽管能够与过度能力网络概括，但深神经网络通常会学会滥用数据中的虚假偏见而不是使用实际的任务相关信息。由于此类快捷方式仅在收集的数据集中有效，因此由此产生的偏置模型在现实世界的投入上表现不佳，或导致意外的社交影响，例如性别歧视。为了抵消偏差的影响，现有方法可以利用辅助信息，这在实践中很少可获得，或者在训练数据中的无偏见样本中筛选，希望能够充分存在清洁样品。但是，这些关于数据的推定并不总是保证。在本文中，我们提出了通过生成偏差变换〜（CDVG）对比下展，该〜（CDVG）能够在现有的方法中经营，其中现有方法由于未偏置的偏差样品而不足的预设而下降。通过我们的观察，不仅如前所述的鉴别模型，而且生成模型倾向于关注偏差，CDVG使用翻译模型来将样本中的偏置转换为另一种偏差模式，同时保留任务相关信息。 。通过对比学习，我们将转化的偏见视图与另一个学习偏见，学习偏见不变的表示。综合和现实世界数据集的实验结果表明，我们的框架优于目前的最先进，并且有效地阻止模型即使在无偏差样本极为稀缺时也会被偏置。

在图像分类中，“ debiasing”旨在训练分类器，以免对数据集偏差，数据样本的外围属性与目标类别之间的强相关性。例如，即使数据集中的青蛙类主要由具有沼泽背景的青蛙图像组成（即，偏见与一致的样本），也应该能够在海滩上正确地对青蛙进行正确分类（即，偏见的样品， ）。最近的辩论方法通常使用两个组件进行偏见，一个有偏见的模型$ f_b $和一个模型$ f_d $。 $ f_b $经过培训，可以专注于偏见的样本（即过度适合偏见），而$ f_d $主要通过专注于$ f_b $未能学习的样品，主要接受了偏见的样本培训，导致$ f_d $。不太容易受到数据集偏差的影响。虽然最先进的偏见技术旨在更好地培训$ f_d $，但我们专注于培训$ f_b $，这是迄今为止被忽视的组件。我们的实证分析表明，从$ f_b $的培训设置中删除偏见的样本对于改善$ f_d $的偏见性能很重要。这是由于以下事实：偏置冲突样品会干扰$ f_b $的偏见，因为这些样本不包括偏差属性。为此，我们提出了一种简单而有效的数据样本选择方法，该方法可以删除偏置冲突的样本，以构建一个偏置放大数据集用于培训$ f_b $。我们的数据示例选择方法可以直接应用于现有的基于重新加权的偏差方法，从而获得一致的性能提升并实现合成和现实世界数据集的最新性能。

图像分类器通常过于依赖于与目标类（即数据集偏差）在预测时具有很强相关性的外围属性。最近，无数的研究着重于缓解此类数据集偏见，其任务被称为偏见。但是，这些偏见方法通常具有不一致的实验设置（例如数据集和神经网络体系结构）。此外，大多数先前关于辩护方面的研究都没有指定它们如何选择涉及早期停止和超参数调整的模型参数。本文的目的是标准化不一致的实验设置，并提出一个用于脱缩的一致模型参数选择标准。基于这种统一的实验设置和模型参数选择标准，我们构建了一个名为DebiasBench的基准测试，其中包括五个数据集和七个Debiasing方法。我们仔细地在各个方面进行了广泛的实验，并表明不同的最新方法分别在不同的数据集中最有效。即使，没有任何依据模块的方法，也显示出低偏置严重程度的数据集中的竞争结果。我们公开释放DebiasBench中现有的辩论方法的实施，以鼓励未来的研究人员进行辩护，以进行公平的比较并进一步推动最先进的表现。

Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias). Recent debiasing techniques have successfully achieved generalization performance by underestimating easy-to-learn samples (i.e., bias-aligned samples) and highlighting difficult-to-learn samples (i.e., bias-conflicting samples). However, these techniques may fail owing to noisy labels, because the trained model recognizes noisy labels as difficult-to-learn and thus highlights them. In this study, we find that earlier approaches that used the provided labels to quantify difficulty could be affected by the small proportion of noisy labels. Furthermore, we find that running denoising algorithms before debiasing is ineffective because denoising algorithms reduce the impact of difficult-to-learn samples, including valuable bias-conflicting samples. Therefore, we propose an approach called denoising after entropy-based debiasing, i.e., DENEB, which has three main stages. (1) The prejudice model is trained by emphasizing (bias-aligned, clean) samples, which are selected using a Gaussian Mixture Model. (2) Using the per-sample entropy from the output of the prejudice model, the sampling probability of each sample that is proportional to the entropy is computed. (3) The final model is trained using existing denoising algorithms with the mini-batches constructed by following the computed sampling probability. Compared to existing debiasing and denoising algorithms, our method achieves better debiasing performance on multiple benchmarks.

在偏置数据集上培训的分类模型通常在分发外部的外部样本上表现不佳，因为偏置的表示嵌入到模型中。最近，已经提出了各种脱叠方法来解除偏见的表示，但仅丢弃偏见的特征是具有挑战性的，而不会改变其他相关信息。在本文中，我们提出了一种新的扩展方法，该方法使用不同标记图像的纹理表示明确地生成附加图像来放大训练数据集，并在训练分类器时减轻偏差效果。每个新的生成图像包含来自源图像的类似内容信息，同时从具有不同标签的目标图像传送纹理。我们的模型包括纹理共发生损耗，该损耗确定生成的图像的纹理是否与目标的纹理类似，以及确定所生成和源图像之间的内容细节是否保留的内容细节的空间自相似性丢失。生成和原始训练图像都进一步用于训练能够改善抗偏置表示的鲁棒性的分类器。我们使用具有已知偏差的五个不同的人工设计数据集来展示我们的方法缓解偏差信息的能力。对于所有情况，我们的方法表现优于现有的现有最先进的方法。代码可用：https://github.com/myeongkyunkang/i2i4debias

深度神经网络令人惊奇地遭受数据集偏见，这对模型鲁棒性，泛化和公平性有害。在这项工作中，我们提出了一个两级的脱扎方案，以防止顽固的未知偏差。通过分析有偏置模型的存在的因素，我们设计了一种小说学习目标，通过依赖单独的偏见，无法达到。具体而言，使用所提出的梯度对准（GA）实现了脱叠模型，该梯度对准（GA）动态地平衡了偏置对齐和偏见冲突的样本的贡献（在整个整个训练过程中，在整个训练过程中，强制执行模型以利用内部提示进行公平的决定。虽然在真实世界的情景中，潜在的偏差非常难以发现并对手动标记昂贵。我们进一步提出了通过对等挑选和培训集合来提出自动偏见冲突的样本挖掘方法，而无需先前了解偏见信息。各种数据中的多个数据集进行的实验表明了我们拟议计划的有效性和稳健性，该计划成功减轻了未知偏差的负面影响，实现了最先进的性能。

Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labels are strongly correlated with undesirable features. To prevent a network from learning such features, recent methods augment training data such that examples displaying spurious correlations (i.e., bias-aligned examples) become a minority, whereas the other, bias-conflicting examples become prevalent. However, these approaches are sometimes difficult to train and scale to real-world data because they rely on generative models or disentangled representations. We propose an alternative based on mixup, a popular augmentation that creates convex combinations of training examples. Our method, coined SelecMix, applies mixup to contradicting pairs of examples, defined as showing either (i) the same label but dissimilar biased features, or (ii) different labels but similar biased features. Identifying such pairs requires comparing examples with respect to unknown biased features. For this, we utilize an auxiliary contrastive model with the popular heuristic that biased features are learned preferentially during training. Experiments on standard benchmarks demonstrate the effectiveness of the method, in particular when label noise complicates the identification of bias-conflicting examples.

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.

神经网络通常使预测依赖于数据集的虚假相关性，而不是感兴趣的任务的内在特性，面对分布外（OOD）测试数据的急剧下降。现有的De-Bias学习框架尝试通过偏置注释捕获特定的DataSet偏差，它们无法处理复杂的“ood方案”。其他人在低能力偏置模型或损失上隐含地识别数据集偏置，但在训练和测试数据来自相同分布时，它们会降低。在本文中，我们提出了一般的贪婪去偏见学习框架（GGD），它贪婪地训练偏置模型和基础模型，如功能空间中的梯度下降。它鼓励基础模型专注于用偏置模型难以解决的示例，从而仍然在测试阶段中的杂散相关性稳健。 GGD在很大程度上提高了各种任务的模型的泛化能力，但有时会过度估计偏置水平并降低在分配测试。我们进一步重新分析了GGD的集合过程，并将课程正规化为由课程学习启发的GGD，这取得了良好的分配和分发性能之间的权衡。对图像分类的广泛实验，对抗问题应答和视觉问题应答展示了我们方法的有效性。 GGD可以在特定于特定于任务的偏置模型的设置下学习更强大的基础模型，其中具有现有知识和自组合偏置模型而无需先验知识。

神经网络倾向于在训练数据的主要部分中表现出的类和潜在属性之间的虚假相关性，这破坏了其概括能力。本文提出了一种新的方法，用于培训错误的分类器，没有虚假属性标签。该方法的关键思想是采用分类器委员会作为辅助模块，该模块可以识别偏置冲突的数据，即没有虚假相关性的数据，并在训练主要分类器时向它们分配了很大的权重。该委员会被学到了一个自举的合奏，因此大多数分类器都具有偏见和多样化，并且故意无法相应地预测偏见的偏见。因此，预测难度委员会的共识为识别和加权偏见冲突数据提供了可靠的提示。此外，该委员会还接受了从主要分类器转移的知识的培训，以便它逐渐与主要分类器一起变得偏见，并强调随着培训的进行而更加困难的数据。在五个现实世界数据集中，我们的方法在没有像我们这样的虚假属性标签的现有方法上优于现有方法，甚至偶尔会超越依靠偏见标签的方法。

分布式概括（OOD）都是关于对环境变化的学习不变性。如果每个类中的上下文分布均匀分布，则OOD将是微不足道的，因为由于基本原则，可以轻松地删除上下文：类是上下文不变的。但是，收集这种平衡的数据集是不切实际的。学习不平衡的数据使模型偏见对上下文，从而伤害了OOD。因此，OOD的关键是上下文平衡。我们认为，在先前工作中广泛采用的假设，可以直接从偏见的类预测中注释或估算上下文偏差，从而使上下文不完整甚至不正确。相比之下，我们指出了上述原则的另一面：上下文对于类也不变，这激励我们将类（已经被标记为已标记的）视为不同环境以解决上下文偏见（没有上下文标签）。我们通过最大程度地减少阶级样本相似性的对比损失，同时确保这种相似性在所有类别中不变，从而实现这一想法。在具有各种上下文偏见和域间隙的基准测试中，我们表明，配备了我们上下文估计的简单基于重新加权的分类器实现了最新的性能。我们在https://github.com/simpleshinobu/irmcon上提供了附录中的理论理由和代码。

已经发现深层图像分类器可以从数据集中学习偏差。为了减轻偏见，大多数以前的方法都需要标签受保护的属性（例如，年龄，肤色）为全套，这有两个限制：1）当标签不可用时，它是不可行的； 2）它们无法缓解未知的偏见 - 人类没有先入为主的偏见。为了解决这些问题，我们提出了偏见的替代网络（Debian），该网络包括两个网络 - 一个发现者和一个分类器。通过以另一种方式培训，发现者试图找到分类器的多个未知偏见，而无需任何偏见注释，分类器的目的是删除发现者确定的偏见。虽然先前的作品评估了单个偏差的结果，但我们创建了多色MNIST数据集，以更好地缓解多偏差设置中的多个偏差，这不仅揭示了以前的方法中的问题，而且还展示了Debian的优势。在同时识别和减轻多种偏见时。我们进一步对现实世界数据集进行了广泛的实验，表明Debian中的发现者可以识别人类可能很难找到的未知偏见。关于辩护，Debian实现了强烈的偏见缓解绩效。

Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distribution. However, this assumption seldom holds in the real world due to distribution shifts. As a result models that rely on this assumption exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models collectively known as -- \textit{domain generalization methods}. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, we argue that it is possible to categorize the methods based on how causality is leveraged in that method and in which part of the model pipeline is it used. To this end, we categorize the causal domain generalization methods into three categories, namely, (i) Invariance via Causal Data Augmentation methods which are applied during the data pre-processing stage, (ii) Invariance via Causal representation learning methods that are utilized during the representation learning stage, and (iii) Invariance via Transferring Causal mechanisms methods that are applied during the classification stage of the pipeline. Furthermore, this survey includes in-depth insights into benchmark datasets and code repositories for domain generalization methods. We conclude the survey with insights and discussions on future directions.

学习公平的代表性对于实现公平或宣传敏感信息至关重要。大多数现有的作品都依靠对抗表示学习将一些不变性注入表示形式。但是，已知对抗性学习方法受到相对不稳定的训练的痛苦，这可能会损害公平性和代表性预测之间的平衡。我们提出了一种新的方法，通过分布对比度变异自动编码器（Farconvae）学习公平表示，该方法诱导潜在空间分解为敏感和非敏感部分。我们首先构建具有不同敏感属性但具有相同标签的观测值。然后，Farconvae强制执行每个不敏感的潜在潜在，而敏感的潜在潜在的潜伏期彼此之间的距离也很远，并且还远离非敏感的潜在通过对比它们的分布。我们提供了一种由高斯和Student-T内核动机的新型对比损失，用于通过理论分析进行分配对比学习。此外，我们采用新的掉期重建损失，进一步提高分解。 Farconvae在公平性，预处理的模型偏差以及来自各种模式（包括表格，图像和文本）的领域概括任务方面表现出了卓越的性能。

机器学习模型通常会遇到与训练分布不同的样本。无法识别分布（OOD）样本，因此将该样本分配给课堂标签会显着损害模​​型的可靠性。由于其对在开放世界中的安全部署模型的重要性，该问题引起了重大关注。由于对所有可能的未知分布进行建模的棘手性，检测OOD样品是具有挑战性的。迄今为止，一些研究领域解决了检测陌生样本的问题，包括异常检测，新颖性检测，一级学习，开放式识别识别和分布外检测。尽管有相似和共同的概念，但分别分布，开放式检测和异常检测已被独立研究。因此，这些研究途径尚未交叉授粉，创造了研究障碍。尽管某些调查打算概述这些方法，但它们似乎仅关注特定领域，而无需检查不同领域之间的关系。这项调查旨在在确定其共同点的同时，对各个领域的众多著名作品进行跨域和全面的审查。研究人员可以从不同领域的研究进展概述中受益，并协同发展未来的方法。此外，据我们所知，虽然进行异常检测或单级学习进行了调查，但没有关于分布外检测的全面或最新的调查，我们的调查可广泛涵盖。最后，有了统一的跨域视角，我们讨论并阐明了未来的研究线，打算将这些领域更加紧密地融为一体。

Neural image classifiers are known to undergo severe performance degradation when exposed to input that exhibits covariate-shift with respect to the training distribution. Successful hand-crafted augmentation pipelines aim at either approximating the expected test domain conditions or to perturb the features that are specific to the training environment. The development of effective pipelines is typically cumbersome, and produce transformations whose impact on the classifier performance are hard to understand and control. In this paper, we show that recent Text-to-Image (T2I) generators' ability to simulate image interventions via natural-language prompts can be leveraged to train more robust models, offering a more interpretable and controllable alternative to traditional augmentation methods. We find that a variety of prompting mechanisms are effective for producing synthetic training data sufficient to achieve state-of-the-art performance in widely-adopted domain-generalization benchmarks and reduce classifiers' dependency on spurious features. Our work suggests that further progress in T2I generation and a tighter integration with other research fields may represent a significant step towards the development of more robust machine learning systems.

对分布（OOD）数据的概括是人类自然的能力，但对于机器而言挑战。这是因为大多数学习算法强烈依赖于i.i.d.〜对源/目标数据的假设，这在域转移导致的实践中通常会违反。域的概括（DG）旨在通过仅使用源数据进行模型学习来实现OOD的概括。在过去的十年中，DG的研究取得了长足的进步，导致了广泛的方法论，例如，基于域的一致性，元学习，数据增强或合奏学习的方法，仅举几例；还在各个应用领域进行了研究，包括计算机视觉，语音识别，自然语言处理，医学成像和强化学习。在本文中，首次提供了DG中的全面文献综述，以总结过去十年来的发展。具体而言，我们首先通过正式定义DG并将其与其他相关领域（如域适应和转移学习）联系起来来涵盖背景。然后，我们对现有方法和理论进行了彻底的审查。最后，我们通过有关未来研究方向的见解和讨论来总结这项调查。

Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation. In this paper, we propose a simple, yet effective method named contrasting shifted instances (CSI), inspired by the recent success on contrastive learning of visual representations. Specifically, in addition to contrasting a given sample with other instances as in conventional contrastive learning methods, our training scheme contrasts the sample with distributionally-shifted augmentations of itself. Based on this, we propose a new detection score that is specific to the proposed training scheme. Our experiments demonstrate the superiority of our method under various novelty detection scenarios, including unlabeled one-class, unlabeled multi-class and labeled multi-class settings, with various image benchmark datasets. Code and pre-trained models are available at https://github.com/alinlab/CSI.

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

Trying to capture the sample-label relationship, conditional generative models often end up inheriting the spurious correlation in the training dataset, giving label-conditional distributions that are severely imbalanced in another latent attribute. To mitigate such undesirable correlations engraved into generative models, which we call spurious causality, we propose a general two-step strategy. (a) Fairness Intervention (FI): Emphasize the minority samples that are hard to be generated due to the spurious correlation in the training dataset. (b) Corrective Sampling (CS): Filter the generated samples explicitly to follow the desired label-conditional latent attribute distribution. We design the fairness intervention for various degrees of supervision on the spurious attribute, including unsupervised, weakly-supervised, and semi-supervised scenarios. Our experimental results show that the proposed FICS can successfully resolve the spurious correlation in generated samples on various datasets.