We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty
estimation using feature space densities, to semantic segmentation. DDU enables
quantifying and disentangling epistemic and aleatoric uncertainty in a single
forward pass through the model. We study the similarity of feature
representations of pixels at different locations for the same class and
conclude that it is feasible to apply DDU location independently, which leads
to a significant reduction in memory consumption compared to pixel dependent
DDU. Using the DeepLab-v3+ architecture on Pascal VOC 2012, we show that DDU
improves upon MC Dropout and Deep Ensembles while being significantly faster to
compute.

本文提出将 Deep Deterministic Uncertainty 扩展至语义分割领域，其能够量化和分离模型的认识不确定性和随机不确定性，同时提出将 DDU 独立于像素位置进行应用，以此减少内存消耗，最后在 Pascal VOC 2012 数据集上使用 DeepLab-v3+ 神经网络结构能够有效提升模型性能与运算速度。

语义分割的深度确定性不确定性

Deep Deterministic Uncertainty for Semantic Segmentation

Reliable uncertainty from deterministic single-forward pass models is sought
after because conventional methods of uncertainty quantification are
computationally expensive. We take two complex single-forward-pass uncertainty
approaches, DUQ and SNGP, and examine whether they mainly rely on a
well-regularized feature space. Crucially, without using their more complex
methods for estimating uncertainty, a single softmax neural net with such a
feature-space, achieved via residual connections and spectral normalization,
*outperforms* DUQ and SNGP's epistemic uncertainty predictions using simple
Gaussian Discriminant Analysis *post-training* as a separate feature-space
density estimator -- without fine-tuning on OoD data, feature ensembling, or
input pre-procressing. This conceptually simple *Deep Deterministic Uncertainty
(DDU)* baseline can also be used to disentangle aleatoric and epistemic
uncertainty and performs as well as Deep Ensembles, the state-of-the art for
uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs
SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O) as well as in active learning
settings across different model architectures, yet is *computationally
cheaper*.

本研究提出了一种概念简单的基线模型，即通过残差连接和谱归一化构建特征空间后，利用高斯判别分析对 softmax 神经网络的预测原型进行后处理，从而达到准确预测深度神经网络中的不确定性的目的，这种 Deep Deterministic Uncertainty（DDU），可以替代当前复杂且耗时的不确定性量化方法，且比当前最先进的方法的计算成本更低。