Predictive uncertainty estimation is essential for deploying Deep Neural
Networks in real-world autonomous systems. However, most successful approaches
are computationally intensive. In this work, we attempt to address these
challenges in the context of autonomous driving perception tasks. Recently
proposed Deterministic Uncertainty Methods (DUM) can only partially meet such
requirements as their scalability to complex computer vision tasks is not
obvious. In this work we advance a scalable and effective DUM for
high-resolution semantic segmentation, that relaxes the Lipschitz constraint
typically hindering practicality of such architectures. We learn a discriminant
latent space by leveraging a distinction maximization layer over an
arbitrarily-sized set of trainable prototypes. Our approach achieves
competitive results over Deep Ensembles, the state-of-the-art for uncertainty
prediction, on image classification, segmentation and monocular depth
estimation tasks. Our code is available at this https URL

该研究提出了一种可扩展的、有效的决策不确定性方法，通过利用区别最大化层，通过任意大小的可训练原型学习鉴别隐藏空间，以解决深度学习在实际中应用时不确定性预测的问题，该方法在图像分类、分割和单眼深度估计任务上超过了深度集合的现有技术。