引导是机器学习和统计信息中的集合和不确定性量化的主要工具。但是，由于其多种培训和重采样的性质，自动启动深度神经网络是计算繁重的;因此，在不确定性估计和相关任务的实际应用中具有困难。为了克服这种计算瓶颈，我们提出了一种名为“Emph {神经盗版的}（Neuboots）的新方法，这将通过单一模型训练来生成引导的神经网络。 Neuboots将引导权重注射到骨干网络的高级特征层中，并输出目标的引导预测，而无需额外的参数和从头开始的重复计算。我们将Neuboots应用于与不确定量化相关的各种机器学习任务，包括图像分类和语义分割，主动学习和分发外样品的预测校准。我们的经验结果表明，Neuboots在较低的计算成本下优于其他基于袋的方法，而不会失去自动启动的有效性。

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

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。

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples. In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier. We obtain the class conditional Gaussian distributions with respect to (low-and upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance. While most prior methods have been evaluated for detecting either out-of-distribution or adversarial samples, but not both, the proposed method achieves the state-of-the-art performances for both cases in our experiments. Moreover, we found that our proposed method is more robust in harsh cases, e.g., when the training dataset has noisy labels or small number of samples. Finally, we show that the proposed method enjoys broader usage by applying it to class-incremental learning: whenever out-of-distribution samples are detected, our classification rule can incorporate new classes well without further training deep models.

The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a model ensemble. In a naive implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where even a single deep network is already demanding in terms of compute and memory, and has given rise to a number of attempts to emulate the model ensemble without actually instantiating separate ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation (FiLM). That technique was originally developed for multi-task learning, with the aim of decoupling different tasks. We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit ensemble methods, and it and comes very close to the upper bound of an explicit ensemble of networks (sometimes even beating it), at a fraction of the memory cost.

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。

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.

It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainly resolve this issue by retraining the entire model to impose the uncertainty quantification capability so that the learned model can achieve desired performance in accuracy and uncertainty prediction simultaneously. However, training the model from scratch is computationally expensive and may not be feasible in many situations. In this work, we consider a more practical post-hoc uncertainty learning setting, where a well-trained base model is given, and we focus on the uncertainty quantification task at the second stage of training. We propose a novel Bayesian meta-model to augment pre-trained models with better uncertainty quantification abilities, which is effective and computationally efficient. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties and easily adapt to different application settings, including out-of-domain data detection, misclassification detection, and trustworthy transfer learning. We demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications over multiple representative image classification benchmarks.

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient descent (SGD) iterates with a modified learning rate schedule, has recently been shown to improve generalization in deep learning. With SWAG, we fit a Gaussian using the SWA solution as the first moment and a low rank plus diagonal covariance also derived from the SGD iterates, forming an approximate posterior distribution over neural network weights; we then sample from this Gaussian distribution to perform Bayesian model averaging. We empirically find that SWAG approximates the shape of the true posterior, in accordance with results describing the stationary distribution of SGD iterates. Moreover, we demonstrate that SWAG performs well on a wide variety of tasks, including out of sample detection, calibration, and transfer learning, in comparison to many popular alternatives including MC dropout, KFAC Laplace, SGLD, and temperature scaling.

预测不确定性估计对于在现实世界自治系统中部署深层神经网络至关重要。但是，大多数成功的方法是计算密集型的。在这项工作中，我们试图在自主驾驶感知任务的背景下解决这些挑战。最近提出的确定性不确定性方法（DUM）只能部分满足其对复杂计算机视觉任务的可扩展性，这并不明显。在这项工作中，我们为高分辨率的语义分割推动了可扩展有效的DUM，它放松了Lipschitz约束通常会阻碍此类架构的实用性。我们通过利用在任意大小的可训练原型集上的区别最大化层来学习判别潜在空间。我们的方法在深层合奏，不确定性预测，图像分类，细分和单眼深度估计任务上取得了竞争成果。我们的代码可在https://github.com/ensta-u2is/ldu上找到

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。

现代深度学习方法构成了令人难以置信的强大工具，以解决无数的挑战问题。然而，由于深度学习方法作为黑匣子运作，因此与其预测相关的不确定性往往是挑战量化。贝叶斯统计数据提供了一种形式主义来理解和量化与深度神经网络预测相关的不确定性。本教程概述了相关文献和完整的工具集，用于设计，实施，列车，使用和评估贝叶斯神经网络，即使用贝叶斯方法培训的随机人工神经网络。

最近出现了一系列用于估计具有单个正向通行证的深神经网络中的认知不确定性的新方法，最近已成为贝叶斯神经网络的有效替代方法。在信息性表示的前提下，这些确定性不确定性方法（DUM）在检测到分布（OOD）数据的同时在推理时添加可忽略的计算成本时实现了强大的性能。但是，目前尚不清楚dums是否经过校准，可以无缝地扩展到现实世界的应用 - 这都是其实际部署的先决条件。为此，我们首先提供了DUMS的分类法，并在连续分配转移下评估其校准。然后，我们将它们扩展到语义分割。我们发现，尽管DUMS尺度到现实的视觉任务并在OOD检测方面表现良好，但当前方法的实用性受到分配变化下的校准不良而破坏的。

部署在医学成像任务上的机器学习模型必须配备分布外检测功能，以避免错误的预测。不确定依赖于深神经网络的分布外检测模型是否适合检测医学成像中的域移位。高斯流程可以通过其数学结构可靠地与分布数据点可靠地分开分发数据点。因此，我们为分层卷积高斯工艺提出了一个参数有效的贝叶斯层，该过程融合了在Wasserstein-2空间中运行的高斯过程，以可靠地传播不确定性。这直接用远距离的仿射操作员在分布中直接取代了高斯流程。我们对脑组织分割的实验表明，所得的架构接近了确定性分割算法（U-NET）的性能，而先前的层次高斯过程尚未实现。此外，通过将相同的分割模型应用于分布外数据（即具有病理学（例如脑肿瘤）的图像），我们表明我们的不确定性估计导致分布外检测，以优于以前的贝叶斯网络和以前的贝叶斯网络的功能基于重建的方法学习规范分布。为了促进未来的工作，我们的代码公开可用。

已经提出了神经常规差分方程（节点）作为流行深度学习模型的连续深度概括，例如残留网络（RESNET）。它们提供参数效率并在一定程度上在深度学习模型中自动化模型选择过程。然而，它们缺乏大量的不确定性建模和稳健性能力，这对于他们在几个现实世界应用中的使用至关重要，例如自主驾驶和医疗保健。我们提出了一种新颖的和独特的方法来通过考虑在ode求解器的结束时间$ t $上的分布来模拟节点的不确定性。所提出的方法，潜在的时间节点（LT节点）将$ T $视为潜在变量，并应用贝叶斯学习，以获得超过数据的$ $ $。特别地，我们使用变分推理来学习近似后的后验和模型参数。通过考虑来自后部的不同样本的节点表示来完成预测，并且可以使用单个向前通过有效地完成。由于$ t $隐含地定义节点的深度，超过$ t $的后部分发也会有助于节点的模型选择。我们还提出了一种自适应潜在的时间节点（Alt-Node），其允许每个数据点在终点上具有不同的后分布。 Alt-Node使用摊销变分推理来使用推理网络学习近似后的后验。我们展示了通过合成和几个现实世界图像分类数据的实验来建立不确定性和鲁棒性的提出方法的有效性。

最近，深度学习中的不确定性估计已成为提高安全至关重要应用的可靠性和鲁棒性的关键领域。尽管有许多提出的方法要么关注距离感知模型的不确定性，要么是分布式检测的不确定性，要么是针对分布校准的输入依赖性标签不确定性，但这两种类型的不确定性通常都是必要的。在这项工作中，我们提出了用于共同建模模型和数据不确定性的HETSNGP方法。我们表明，我们提出的模型在这两种类型的不确定性之间提供了有利的组合，因此在包括CIFAR-100C，ImagEnet-C和Imagenet-A在内的一些具有挑战性的分发数据集上优于基线方法。此外，我们提出了HETSNGP Ensemble，这是我们方法的结合版本，该版本还对网络参数的不确定性进行建模，并优于其他集合基线。

独立训练的神经网络的集合是一种最新的方法，可以在深度学习中估算预测性不确定性，并且可以通过三角洲函数的混合物解释为后验分布的近似值。合奏的培训依赖于损失景观的非跨性别性和其单个成员的随机初始化，从而使后近似不受控制。本文提出了一种解决此限制的新颖和原则性的方法，最大程度地减少了函数空间中真实后验和内核密度估计器（KDE）之间的$ f $ divergence。我们从组合的角度分析了这一目标，并表明它在任何$ f $的混合组件方面都是supporular。随后，我们考虑了贪婪合奏结构的问题。从负$ f $ didivergence上的边际增益来量化后近似的改善，通过将新组件添加到KDE中得出，我们得出了集合方法的新型多样性项。我们的方法的性能在计算机视觉的分布外检测基准测试中得到了证明，该基准在多个数据集中训练的一系列架构中。我们方法的源代码可在https://github.com/oulu-imeds/greedy_ensembles_training上公开获得。

Acquiring labeled data is challenging in many machine learning applications with limited budgets. Active learning gives a procedure to select the most informative data points and improve data efficiency by reducing the cost of labeling. The info-max learning principle maximizing mutual information such as BALD has been successful and widely adapted in various active learning applications. However, this pool-based specific objective inherently introduces a redundant selection and further requires a high computational cost for batch selection. In this paper, we design and propose a new uncertainty measure, Balanced Entropy Acquisition (BalEntAcq), which captures the information balance between the uncertainty of underlying softmax probability and the label variable. To do this, we approximate each marginal distribution by Beta distribution. Beta approximation enables us to formulate BalEntAcq as a ratio between an augmented entropy and the marginalized joint entropy. The closed-form expression of BalEntAcq facilitates parallelization by estimating two parameters in each marginal Beta distribution. BalEntAcq is a purely standalone measure without requiring any relational computations with other data points. Nevertheless, BalEntAcq captures a well-diversified selection near the decision boundary with a margin, unlike other existing uncertainty measures such as BALD, Entropy, or Mean Standard Deviation (MeanSD). Finally, we demonstrate that our balanced entropy learning principle with BalEntAcq consistently outperforms well-known linearly scalable active learning methods, including a recently proposed PowerBALD, a simple but diversified version of BALD, by showing experimental results obtained from MNIST, CIFAR-100, SVHN, and TinyImageNet datasets.

已知现代深度神经网络模型将错误地将分布式（OOD）测试数据分类为具有很高信心的分数（ID）培训课程之一。这可能会对关键安全应用产生灾难性的后果。一种流行的缓解策略是训练单独的分类器，该分类器可以在测试时间检测此类OOD样本。在大多数实际设置中，在火车时间尚不清楚OOD的示例，因此，一个关键问题是：如何使用合成OOD样品来增加ID数据以训练这样的OOD检测器？在本文中，我们为称为CNC的OOD数据增强提出了一种新颖的复合腐败技术。 CNC的主要优点之一是，除了培训集外，它不需要任何固定数据。此外，与当前的最新技术（SOTA）技术不同，CNC不需要在测试时间进行反向传播或结合，从而使我们的方法在推断时更快。我们与过去4年中主要会议的20种方法进行了广泛的比较，表明，在OOD检测准确性和推理时间方面，使用基于CNC的数据增强训练的模型都胜过SOTA。我们包括详细的事后分析，以研究我们方法成功的原因，并确定CNC样本的较高相对熵和多样性是可能的原因。我们还通过对二维数据集进行零件分解分析提供理论见解，以揭示（视觉和定量），我们的方法导致ID类别周围的边界更紧密，从而更好地检测了OOD样品。源代码链接：https：//github.com/cnc-ood

Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt to model uncertainty due to distributional mismatch either implicitly through model uncertainty or as data uncertainty. This work proposes a new framework for modeling predictive uncertainty called Prior Networks (PNs) which explicitly models distributional uncertainty. PNs do this by parameterizing a prior distribution over predictive distributions. This work focuses on uncertainty for classification and evaluates PNs on the tasks of identifying out-of-distribution (OOD) samples and detecting misclassification on the MNIST and CIFAR-10 datasets, where they are found to outperform previous methods. Experiments on synthetic and MNIST data show that unlike previous non-Bayesian methods PNs are able to distinguish between data and distributional uncertainty.