论文标题
乌克根：一个统一的模型，用于通过有条件gan训练的深层分类器的不确定性定量
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
论文作者
论文摘要
我们提出了一种基于生成对抗网络（GAN）的图像分类中深层神经网络的核心和认知不确定性的方法。尽管文献中的大多数作品都使用gans产生分布（OOD）的示例仅着眼于OOD检测的评估，但我们提出了一种基于GAN的方法来学习一个分类器，该分类器对OOD示例产生适当的不确定性以及误报（FPS）。我们没有用最新的gan生成的ood示例来屏蔽整个分布数据，而是用条件gan生成的课堂外示例分别屏蔽每个类，并用一个单vs-all Image分类器对此进行补充。在我们的实验中，尤其是在CIFAR10，CIFAR100和Tiny Imagenet上，我们在基于GAN训练的分类器的OOD检测和FP检测性能方面进行了改进。此外，我们还发现，生成的GAN示例不会显着影响分类器的校准误差，并导致模型准确性的显着提高。
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.
