论文标题
潜在的判别确定性不确定性
Latent Discriminant deterministic Uncertainty
论文作者
论文摘要
预测不确定性估计对于在现实世界自主系统中部署深层神经网络至关重要。但是，大多数成功的方法是计算密集型的。在这项工作中，我们试图在自主驾驶感知任务的背景下解决这些挑战。最近提出的确定性不确定性方法（DUM）只能部分满足其对复杂计算机视觉任务的可扩展性的要求。在这项工作中，我们为高分辨率的语义分割推动了可扩展有效的dum，它放松了Lipschitz的约束通常会阻碍此类架构的实用性。我们通过利用在任意大小的可训练原型集上的区别最大化层来学习判别潜在空间。我们的方法取得了竞争成果，即深层合奏，不确定性预测，图像分类，细分和单眼深度估计任务的最新结果。我们的代码可在https://github.com/ensta-u2is/ldu上找到
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 https://github.com/ENSTA-U2IS/LDU
