Convolutional neural networks applied for real-world classification tasks need to recognize inputs that are far or out-of-distribution (OoD) with respect to the known or training data. To achieve this, many methods estimate class-conditional posterior probabilities and use confidence scores obtained from the posterior distributions. Recent works propose to use multivariate Gaussian distributions as models of posterior distributions at different layers of the CNN (i.e., for low- and upper-level features), which leads to the confidence scores based on the Mahalanobis distance. However, this procedure involves estimating probability density in high dimensional data using the insufficient number of observations (e.g. the dimensionality of features at the last two layers in the ResNet-101 model are 2048 and 1024, with ca. 1000 observations per class used to estimate density). In this work, we want to address this problem. We show that in many OoD studies in high-dimensional data, LOF-based (Local Outlierness-Factor) methods outperform the parametric, Mahalanobis distance-based methods. This motivates us to propose the nonparametric, LOF-based method of generating the confidence scores for CNNs. We performed several feasibility studies involving ResNet-101 and EffcientNet-B3, based on CIFAR-10 and ImageNet (as known data), and CIFAR-100, SVHN, ImageNet2010, Places365, or ImageNet-O (as outliers). We demonstrated that nonparametric LOF-based confidence estimation can improve current Mahalanobis-based SOTA or obtain similar performance in a simpler way.

翻译：用于真实世界分类任务的 convolutional 神经网络需要识别在已知或培训数据方面远至或超出分布范围的投入(OoD),为此,许多方法都估算了等级条件的事后概率,并使用了从事后分布中获得的信任分数。最近的工作提议使用多种变式高斯分布作为CNN不同层次(即低和上级特征)的红外分布模型,这导致基于Mahalanobis距离的可信度分数。然而,这一程序涉及利用观测数量不足(例如ResNet-101模型最后两个层次的特征的维度为2048和1024, 用于估计密度的每类观测量为1000)。 在这项工作中,我们想解决这个问题。 在基于高度数据、基于LOF的LOF和基于(地方局外向)的OOD研究中, 超过参数的高度数据密度。 Mahalanobisxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx