In this paper, we propose a simple yet effective method to deal with the
violation of the Closed-World Assumption for a classifier. Previous works tend
to apply a threshold either on the classification scores or the loss function
to reject the inputs that violate the assumption. However, these methods cannot
achieve the low False Positive Ratio (FPR) required in safety applications. The
proposed method is a rejection option based on hypothesis testing with
probabilistic networks. With probabilistic networks, it is possible to estimate
the distribution of outcomes instead of a single output. By utilizing Z-test
over the mean and standard deviation for each class, the proposed method can
estimate the statistical significance of the network certainty and reject
uncertain outputs. The proposed method was experimented on with different
configurations of the COCO and CIFAR datasets. The performance of the proposed
method is compared with the Softmax Response, which is a known top-performing
method. It is shown that the proposed method can achieve a broader range of
operation and cover a lower FPR than the alternative.

本论文提出一种鉴别器违反封闭世界假设的有效方法，该方法基于概率网络的假设检验拒绝了不确定的输出并实现了低误报率的要求。

基于假设检验的选择性概率分类器

Selective Probabilistic Classifier Based on Hypothesis Testing

In semantic segmentation tasks, input images can often have more than one
plausible interpretation, thus allowing for multiple valid labels. To capture
such ambiguities, recent work has explored the use of probabilistic networks
that can learn a distribution over predictions. However, these do not
necessarily represent the empirical distribution accurately. In this work, we
present a strategy for learning a calibrated predictive distribution over
semantic maps, where the probability associated with each prediction reflects
its ground truth correctness likelihood. To this end, we propose a novel
two-stage, cascaded approach for calibrated adversarial refinement: (i) a
standard segmentation network is trained with categorical cross entropy to
predict a pixelwise probability distribution over semantic classes and (ii) an
adversarially trained stochastic network is used to model the inter-pixel
correlations to refine the output of the first network into coherent samples.
Importantly, to calibrate the refinement network and prevent mode collapse, the
expectation of the samples in the second stage is matched to the probabilities
predicted in the first. We demonstrate the versatility and robustness of the
approach by achieving state-of-the-art results on the multigrader LIDC dataset
and on a modified Cityscapes dataset with injected ambiguities. In addition, we
show that the core design can be adapted to other tasks requiring learning a
calibrated predictive distribution by experimenting on a toy regression
dataset. We provide an open source implementation of our method at
this https URL

该研究提出了一种通过使用概率网络来学习预测分布，以实现语义地图上的校准预测分布的策略，并使用对抗性训练来精炼输出，并在多分类 LIDC 数据集和注入模糊性的 Cityscapes 数据集上取得最先进结果。

随机语义分割的校准对抗修正

Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Techniques for understanding the functioning of complex machine learning
models are becoming increasingly popular, not only to improve the validation
process, but also to extract new insights about the data via exploratory
analysis. Though a large class of such tools currently exists, most assume that
predictions are point estimates and use a sensitivity analysis of these
estimates to interpret the model. Using lightweight probabilistic networks we
show how including prediction uncertainties in the sensitivity analysis leads
to: (i) more robust and generalizable models; and (ii) a new approach for model
interpretation through uncertainty decomposition. In particular, we introduce a
new regularization that takes both the mean and variance of a prediction into
account and demonstrate that the resulting networks provide improved
generalization to unseen data. Furthermore, we propose a new technique to
explain prediction uncertainties through uncertainties in the input domain,
thus providing new ways to validate and interpret deep learning models.

利用轻量级概率网络，将预测不确定性纳入灵敏度分析以及通过不确定性分解的新方法进行模型解释，最终提高了模型的鲁棒性和推广能力，并从输入域的不确定性解释预测不确定性，从而提供了验证和解释深度学习模型的新方法。