神经网络在许多领域都很受欢迎，但它们具有对远离训练数据的示例提供高信心响应的问题。这使得神经网络在其预测中非常有信心的同时进行错误，从而限制了它们对自主驾驶，空间探索等的安全关键应用的可靠性，我们提出了具有标准点产品的神经元泛化基于神经元和RBF神经元作为形状参数的两个极端情况。使用Relu作为激活功能，我们获得具有紧凑载体的新型神经元，这意味着其输出在有界域之外为零。我们展示了如何通过首先训练标准神经网络训练这种神经元的神经网络训练神经网络的困难，然后逐渐将形状参数逐渐增加到所需值。我们还证明，使用所提出的神经元的神经网络具有通用近似性，这意味着它可以近似具有任意精度的任何连续和可积的功能。通过对标准基准数据集的实验，我们展示了所提出的方法的承诺，因为它可以在分布式样品上具有良好的预测准确性，同时能够始终如一地检测并对分布外样品具有低置信度。

Classifiers used in the wild, in particular for safetycritical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data. We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and test error on the original classification task compared to standard training.

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles in out of distribution detection on notable difficult dataset pairs such as Fashion-MNIST vs. MNIST, and CIFAR-10 vs. SVHN.

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

Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles and robots. Existing approaches to detect OOD samples treat a DNN as a black box and assess the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNN are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU based architectures. The proposed method does not introduce high computational workload due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets. ion.

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

Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. We demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images.

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.

目前的分销检测方法通常存在特殊要求（例如，收集异常数据和近似数计验证）并产生副作用（例如，分类精度下降和缓慢/低效推论）。最近，已经提出了熵外检测作为无缝方法（即，避免所有先前提到的缺点的解决方案）。熵外检测解决方案使用ISOMAX损失进行培训和分布外检测的熵分。 ISOMAX损失作为软墨损失的替换（即输出线性层，Softmax激活和跨熵损失的组合）作为替换，因为随着ISOMAX损失的交换软墨损失不需要变化模型的架构或培训程序/超级参数。在本文中，我们执行我们所谓的ISOMAX损失中使用的距离的成像化。此外，我们提出更换最小距离分数的熵分数。实验表明，这些修改显着增加了分布的检测性能，同时保持解决方案无缝。除了竞争或优于所有主要目前的方法外，提出的解决方案除了更容易使用之外，还避免了所有当前限制，因为只需要对培训神经网络的简单损失替代品。用ISOMAX +丢失替换SoftMax丢失并重现结果的代码可在https://github.com/dlmacedo/entropic-out-of-distribution-detection。

由于其在现实世界应用程序中部署机器学习模型中的重要性，因此无法分布（OOD）检测最近受到了机器学习社区的关注。在本文中，我们通过对特征的分布进行建模，提出了一种不确定性量化方法。我们进一步结合了一种有效的合奏机制，即批处理 - 构造批处理的随机神经网络（BE-SNN）并克服特征崩溃问题。我们将提出的BE-SNN的性能与其他最先进的方法进行了比较，并表明BE-SNN在几个OOD基准上产生了卓越的性能，例如两个漫画数据集，FashionMnist，FashionMnist vs Mnist Dataset，FashionMnistvs notmnist数据集和CIFAR10 vs SVHN数据集。

Commonly used AI networks are very self-confident in their predictions, even when the evidence for a certain decision is dubious. The investigation of a deep learning model output is pivotal for understanding its decision processes and assessing its capabilities and limitations. By analyzing the distributions of raw network output vectors, it can be observed that each class has its own decision boundary and, thus, the same raw output value has different support for different classes. Inspired by this fact, we have developed a new method for out-of-distribution detection. The method offers an explanatory step beyond simple thresholding of the softmax output towards understanding and interpretation of the model learning process and its output. Instead of assigning the class label of the highest logit to each new sample presented to the network, it takes the distributions over all classes into consideration. A probability score interpreter (PSI) is created based on the joint logit values in relation to their respective correct vs wrong class distributions. The PSI suggests whether the sample is likely to belong to a specific class, whether the network is unsure, or whether the sample is likely an outlier or unknown type for the network. The simple PSI has the benefit of being applicable on already trained networks. The distributions for correct vs wrong class for each output node are established by simply running the training examples through the trained network. We demonstrate our OOD detection method on a challenging transmission electron microscopy virus image dataset. We simulate a real-world application in which images of virus types unknown to a trained virus classifier, yet acquired with the same procedures and instruments, constitute the OOD samples.

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。

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distribution (OoD) examples only focus on the evaluation of OoD detection, we present a GAN based approach to learn a classifier that produces proper uncertainties for OoD examples as well as for false positives (FPs). Instead of shielding the entire in-distribution data with GAN generated OoD examples which is state-of-the-art, we shield each class separately with out-of-class examples generated by a conditional GAN and complement this with a one-vs-all image classifier. In our experiments, in particular on CIFAR10, CIFAR100 and Tiny ImageNet, we improve over the OoD detection and FP detection performance of state-of-the-art GAN-training based classifiers. Furthermore, we also find that the generated GAN examples do not significantly affect the calibration error of our classifier and result in a significant gain in model accuracy.

已知神经网络模型加强隐藏的数据偏差，使它们不可靠且难以解释。我们试图通过在功能空间中引入归纳偏差来构建“知道他们不知道的内容”。我们表明贝叶斯神经网络的定期激活功能在网络权重和平移 - 不变，静止的高斯过程前沿建立了连接之间的连接。此外，我们表明，通过覆盖三角波和周期性的Relu激活功能，该链接超出了正弦波（傅里叶）激活。在一系列实验中，我们表明定期激活功能获得了域内数据的可比性，并捕获对深度神经网络中的扰动输入的灵敏度进行域名检测。

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。

建立强大的确定性神经网络仍然是一个挑战。一方面，某些方法以降低某些情况下的分类准确性为代价改善了分布检测。另一方面，某些方法同时提高了分类准确性，不确定性估计和分布外检测，但以降低推理效率为代价。在本文中，我们提出了使用Dismax损失的培训确定性神经网络，这是对通常的软马克斯损失的倒入替换（即，线性输出层的组合，软磁性激活和交叉透射率损失） 。从Isomax+损失开始，我们根据所有原型的距离创建每个logit，而不仅仅是与正确类关联的logit。我们还引入了一种结合图像的机制，以构建所谓的分数概率正则化。此外，我们提出了一种快速训练后校准网络的方法。最后，我们提出一个复合分数以执行分布外检测。我们的实验表明，Dismax通常在分类准确性，不确定性估计和分布外检测方面同时优于当前方法，同时保持确定性的神经网络推断效率。重现结果的代码可在https://github.com/dlmacedo/distinction-maximization-loss上获得。

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。

深度神经网络对各种任务取得了出色的性能，但它们具有重要问题：即使对于完全未知的样本，也有过度自信的预测。已经提出了许多研究来成功过滤出这些未知的样本，但它们仅考虑狭窄和特定的任务，称为错误分类检测，开放式识别或分布外检测。在这项工作中，我们认为这些任务应该被视为根本存在相同的问题，因为理想的模型应该具有所有这些任务的检测能力。因此，我们介绍了未知的检测任务，以先前的单独任务的整合，用于严格检查深度神经网络对广谱的广泛未知样品的检测能力。为此，构建了不同尺度上的统一基准数据集，并且存在现有流行方法的未知检测能力进行比较。我们发现深度集合始终如一地优于检测未知的其他方法;但是，所有方法只针对特定类型的未知方式成功。可重复的代码和基准数据集可在https://github.com/daintlab/unknown-detection-benchmarks上获得。

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。