深度神经网络具有令人印象深刻的性能,但是他们无法可靠地估计其预测信心,从而限制了其在高风险领域中的适用性。我们表明,应用多标签的一VS损失揭示了分类的歧义并降低了模型的过度自信。引入的Slova(单标签One-Vs-All)模型重新定义了单个标签情况的典型单VS-ALL预测概率,其中只有一个类是正确的答案。仅当单个类具有很高的概率并且其他概率可忽略不计时,提议的分类器才有信心。与典型的SoftMax函数不同,如果所有其他类的概率都很小,Slova自然会检测到分布的样本。该模型还通过指数校准进行了微调,这使我们能够与模型精度准确地对齐置信分数。我们在三个任务上验证我们的方法。首先,我们证明了斯洛伐克与最先进的分布校准具有竞争力。其次,在数据集偏移下,斯洛伐克的性能很强。最后,我们的方法在检测到分布样品的检测方面表现出色。因此,斯洛伐克是一种工具,可以在需要不确定性建模的各种应用中使用。
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Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive uncertainty. Quantifying uncertainty is especially critical in real-world settings, which often involve input distributions that are shifted from the training distribution due to a variety of factors including sample bias and non-stationarity. In such settings, well calibrated uncertainty estimates convey information about when a model's output should (or should not) be trusted. Many probabilistic deep learning methods, including Bayesian-and non-Bayesian methods, have been proposed in the literature for quantifying predictive uncertainty, but to our knowledge there has not previously been a rigorous largescale empirical comparison of these methods under dataset shift. We present a largescale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of dataset shift on accuracy and calibration. We find that traditional post-hoc calibration does indeed fall short, as do several other previous methods. However, some methods that marginalize over models give surprisingly strong results across a broad spectrum of tasks.
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Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles. However their practicality in real-time, industrial-scale applications are limited due to the high memory and computational cost. Furthermore, ensembles and BNNs do not necessarily fix all the issues with the underlying member networks. In this work, we study principled approaches to improve uncertainty property of a single network, based on a single, deterministic representation. By formalizing the uncertainty quantification as a minimax learning problem, we first identify distance awareness, i.e., the model's ability to quantify the distance of a testing example from the training data, as a necessary condition for a DNN to achieve high-quality (i.e., minimax optimal) uncertainty estimation. We then propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs with two simple changes: (1) applying spectral normalization to hidden weights to enforce bi-Lipschitz smoothness in representations and (2) replacing the last output layer with a Gaussian process layer. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection. Furthermore, SNGP provides complementary benefits to popular techniques such as deep ensembles and data augmentation, making it a simple and scalable building block for probabilistic deep learning. Code is open-sourced at https://github.com/google/uncertainty-baselines
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Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN 1 [21], proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.
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The ability to quickly and accurately identify covariate shift at test time is a critical and often overlooked component of safe machine learning systems deployed in high-risk domains. While methods exist for detecting when predictions should not be made on out-of-distribution test examples, identifying distributional level differences between training and test time can help determine when a model should be removed from the deployment setting and retrained. In this work, we define harmful covariate shift (HCS) as a change in distribution that may weaken the generalization of a predictive model. To detect HCS, we use the discordance between an ensemble of classifiers trained to agree on training data and disagree on test data. We derive a loss function for training this ensemble and show that the disagreement rate and entropy represent powerful discriminative statistics for HCS. Empirically, we demonstrate the ability of our method to detect harmful covariate shift with statistical certainty on a variety of high-dimensional datasets. Across numerous domains and modalities, we show state-of-the-art performance compared to existing methods, particularly when the number of observed test samples is small.
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我们表明,著名的混音的有效性[Zhang等,2018],如果而不是将其用作唯一的学习目标,就可以进一步改善它,而是将其用作标准跨侧面损失的附加规则器。这种简单的变化不仅提供了太大的准确性,而且在大多数情况下,在各种形式的协变量转移和分布外检测实验下,在大多数情况下,混合量的预测不确定性估计质量都显着提高了。实际上,我们观察到混合物在检测出分布样本时可能会产生大量退化的性能,因为我们在经验上表现出来,因为它倾向于学习在整个过程中表现出高渗透率的模型。很难区分分布样本与近分离样本。为了显示我们的方法的功效(RegMixup),我们在视觉数据集(Imagenet&Cifar-10/100)上提供了详尽的分析和实验,并将其与最新方法进行比较,以进行可靠的不确定性估计。
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随着神经网络分类器部署在现实世界应用中,它们可以可靠地检测到它们的故障至关重要。一个实际解决方案是为每个预测分配置信度分数,然后使用这些分数来过滤可能的错误分类。然而,现有的置信度量尚未充分可靠地对此作用。本文介绍了一种新的框架,可以产生用于检测错误分类错误的定量度量。此框架红色在基本分类器的顶部构建错误检测器,并估计使用高斯过程的检测分数的不确定性。在125 UCI数据集上具有其他错误检测方法的实验比较证明了这种方法是有效的。在两个概率基础分类器上进一步实现以及视觉任务中的两个大型深度学习架构进一步证实了该方法是坚固且可扩展的。第三,用分布外和对抗样本的红色的实证分析表明,该方法不仅可以检测错误,还可以使用,而且可以了解它们来自哪里。因此,红色可以使用未来更广泛地提高神经网络分类器的可信度。
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深度神经网络易于对异常值过度自信的预测。贝叶斯神经网络和深度融合都已显示在某种程度上减轻了这个问题。在这项工作中,我们的目标是通过提议预测由高斯混合模型的后续的高斯混合模型来结合这两种方法的益处,该高斯混合模型包括独立培训的深神经网络的LAPPALL近似的加权和。该方法可以与任何一组预先训练的网络一起使用,并且与常规合并相比,只需要小的计算和内存开销。理论上我们验证了我们的方法从训练数据中的培训数据和虚拟化的基本线上的标准不确定量级基准测试中的“远离”的过度控制。
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本文我们的目标是利用异质的温度缩放作为校准策略(OOD)检测。此处的异质性是指每个样品的最佳温度参数可能不同,而不是传统的方法对整个分布使用相同的值。为了实现这一目标,我们提出了一种称为锚定的新培训策略,可以估算每个样品的适当温度值,从而导致几个基准的最新OOD检测性能。使用NTK理论,我们表明该温度函数估计与分类器的认知不确定性紧密相关,这解释了其行为。与某些表现最佳的OOD检测方法相反,我们的方法不需要暴露于其他离群数据集,自定义校准目标或模型结合。通过具有不同OOD检测设置的经验研究 - 远处,OOD附近和语义相干OOD - 我们建立了一种高效的OOD检测方法。可以在此处访问代码和模型-https://github.com/rushilanirudh/amp
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Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.
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人工智能的最新趋势是将验证的模型用于语言和视觉任务,这些模型已经实现了非凡的表现,但也令人困惑。因此,以各种方式探索这些模型的能力对该领域至关重要。在本文中,我们探讨了模型的可靠性,在其中我们将可靠的模型定义为一个不仅可以实现强大的预测性能,而且在许多涉及不确定性(例如选择性预测,开放式设置识别)的决策任务上,在许多决策任务上表现出色,而且表现良好。强大的概括(例如,准确性和适当的评分规则,例如在分布数据集中和分发数据集上的对数可能性)和适应性(例如,主动学习,几乎没有射击不确定性)。我们设计了40个数据集的10种任务类型,以评估视觉和语言域上可靠性的不同方面。为了提高可靠性,我们分别开发了VIT-PLEX和T5-PLEX,分别针对视觉和语言方式扩展了大型模型。 PLEX极大地改善了跨可靠性任务的最先进,并简化了传统协议,因为它可以改善开箱即用的性能,并且不需要设计分数或为每个任务调整模型。我们演示了高达1B参数的模型尺寸的缩放效果,并预处理数据集大小最多4B示例。我们还展示了PLEX在具有挑战性的任务上的功能,包括零射门的开放式识别,主动学习和对话语言理解中的不确定性。
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由于其实际重要性,在提高神经网络安全部署方面的实际重要性,最近经济分配(OOD)检测最近受到了很大的关注。其中一个主要挑战是模型往往会对OOD数据产生高度自信的预测,这在ood检测中破坏了驾驶原理,即该模型应该仅对分布式样品充满信心。在这项工作中,我们提出了反应 - 一种简单有效的技术,用于减少对数据数据的模型过度限制。我们的方法是通过关于神经网络内部激活的新型分析,其为OOD分布显示出高度独特的签名模式。我们的方法可以有效地拓展到不同的网络架构和不同的OOD检测分数。我们经验证明,反应在全面的基准数据集套件上实现了竞争检测性能,并为我们的方法进行了理论解释。与以前的最佳方法相比,在ImageNet基准测试中,反应将假阳性率(FPR95)降低25.05%。
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检测分销(OOD)输入是安全部署现实世界中的机器学习模型的中央挑战。以前的方法通常依赖于从过度分辨率的重量空间衍生的评分,同时在很大程度上忽略了稀疏的作用。在本文中,我们揭示了重要的见解,即依赖对不重要的权重和单位可以直接归因于“ood检测的脆性”。为了减轻这个问题,我们提出了一个基于稀疏的oo ood检测框架被称为骰子。我们的关键思想是基于贡献的衡量标准进行排序,并选择性地使用最突出的重量来导出OOD检测的输出。我们提供了实证和理论洞察力,表征和解释了骰子改善的机制。通过修剪嘈杂的信号,骰子可否降低OOD数据的输出方差,从而导致输出分布和更强的ID数据可分离。骰子表现出色,与先前的最佳方法相比,将FPR95减少至多24.69%。
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神经网络缺乏对抗性鲁棒性,即,它们容易受到对抗的例子,通过对输入的小扰动导致错误的预测。此外,当模型给出错误的预测时,信任被破坏,即,预测的概率不是我们应该相信我们模型的良好指标。在本文中,我们研究了对抗性鲁棒性和校准之间的联系,发现模型对小扰动敏感的输入(很容易攻击)更有可能具有较差的预测。基于这种洞察力,我们通过解决这些对抗的缺陷输入来研究校准。为此,我们提出了基于对抗基于对抗的自适应标签平滑(AR-AD),其通过适应性软化标签,通过适应性软化标签来整合对抗性鲁棒性和校准到训练中的相关性,这是基于对敌人可以攻击的容易攻击。我们发现我们的方法,考虑了分销数据的对抗性稳健性,即使在分布班次下也能够更好地校准模型。此外,还可以应用于集合模型,以进一步提高模型校准。
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Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data. We propose a unified framework for OOD detection that uses an energy score. We show that energy scores better distinguish in-and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue. Within this framework, energy can be flexibly used as a scoring function for any pre-trained neural classifier as well as a trainable cost function to shape the energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained WideResNet, using the energy score reduces the average FPR (at TPR 95%) by 18.03% compared to the softmax confidence score. With energy-based training, our method outperforms the state-of-the-art on common benchmarks.
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随着我们远离数据,预测不确定性应该增加,因为各种各样的解释与鲜为人知的信息一致。我们引入了远距离感知的先验(DAP)校准,这是一种纠正训练域之外贝叶斯深度学习模型过度自信的方法。我们将DAPS定义为模型参数的先验分布,该模型参数取决于输入,通过其与训练集的距离度量。DAP校准对后推理方法不可知,可以作为后处理步骤进行。我们证明了其在各种分类和回归问题中对几个基线的有效性,包括旨在测试远离数据的预测分布质量的基准。
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Confidence calibration -the problem of predicting probability estimates representative of the true correctness likelihood -is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-ofthe-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -a singleparameter variant of Platt Scaling -is surprisingly effective at calibrating predictions.
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检测到分布输入对于在现实世界中安全部署机器学习模型至关重要。然而,已知神经网络遭受过度自信的问题,在该问题中,它们对分布和分布的输入的信心异常高。在这项工作中,我们表明,可以通过在训练中实施恒定的向量规范来通过logit归一化(logitnorm)(logitnorm)来缓解此问题。我们的方法是通过分析的激励,即logit的规范在训练过程中不断增加,从而导致过度自信的产出。因此,LogitNorm背后的关键思想是将网络优化期间输出规范的影响解散。通过LogitNorm培训,神经网络在分布数据和分布数据之间产生高度可区分的置信度得分。广泛的实验证明了LogitNorm的优势,在公共基准上,平均FPR95最高为42.30%。
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现在通常用于高风险设置,如医疗诊断,如医疗诊断,那么需要不确定量化,以避免后续模型失败。无分发的不确定性量化(无分布UQ)是用户友好的范式,用于为这种预测创建统计上严格的置信区间/集合。批判性地,间隔/集合有效而不进行分布假设或模型假设,即使具有最多许多DataPoints也具有显式保证。此外,它们适应输入的难度;当输入示例很困难时,不确定性间隔/集很大,信号传达模型可能是错误的。在没有多大的工作和没有再培训的情况下,可以在任何潜在的算法(例如神经网络)上使用无分​​发方法,以产生置信度集,以便包含用户指定概率,例如90%。实际上,这些方法易于理解和一般,应用于计算机视觉,自然语言处理,深度加强学习等领域出现的许多现代预测问题。这种实践介绍是针对对无需统计学家的免费UQ的实际实施感兴趣的读者。我们通过实际的理论和无分发UQ的应用领导读者,从保形预测开始,并使无关的任何风险的分布控制,如虚假发现率,假阳性分布检测,等等。我们将包括Python中的许多解释性插图,示例和代码样本,具有Pytorch语法。目标是提供读者对无分配UQ的工作理解,使它们能够将置信间隔放在算法上,其中包含一个自包含的文档。
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分布(OOD)检测对于确保机器学习系统的可靠性和安全性至关重要。例如,在自动驾驶中,我们希望驾驶系统在发现在训练时间中从未见过的异常​​场景或对象时,发出警报并将控件移交给人类,并且无法做出安全的决定。该术语《 OOD检测》于2017年首次出现,此后引起了研究界的越来越多的关注,从而导致了大量开发的方法,从基于分类到基于密度到基于距离的方法。同时,其他几个问题,包括异常检测(AD),新颖性检测(ND),开放式识别(OSR)和离群检测(OD)(OD),在动机和方法方面与OOD检测密切相关。尽管有共同的目标,但这些主题是孤立发展的,它们在定义和问题设定方面的细微差异通常会使读者和从业者感到困惑。在这项调查中,我们首先提出一个称为广义OOD检测的统一框架,该框架涵盖了上述五个问题,即AD,ND,OSR,OOD检测和OD。在我们的框架下,这五个问题可以看作是特殊情况或子任务,并且更容易区分。然后,我们通过总结了他们最近的技术发展来审查这五个领域中的每一个,特别关注OOD检测方法。我们以公开挑战和潜在的研究方向结束了这项调查。
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