Equipping predicted segmentation with calibrated uncertainty is essential for
safety-critical applications. In this work, we focus on capturing the
data-inherent uncertainty (aka aleatoric uncertainty) in segmentation,
typically when ambiguities exist in input images. Due to the high-dimensional
output space and potential multiple modes in segmenting ambiguous images, it
remains challenging to predict well-calibrated uncertainty for segmentation. To
tackle this problem, we propose a novel mixture of stochastic experts (MoSE)
model, where each expert network estimates a distinct mode of the aleatoric
uncertainty and a gating network predicts the probabilities of an input image
being segmented in those modes. This yields an efficient two-level uncertainty
representation. To learn the model, we develop a Wasserstein-like loss that
directly minimizes the distribution distance between the MoSE and ground truth
annotations. The loss can easily integrate traditional segmentation quality
measures and be efficiently optimized via constraint relaxation. We validate
our method on the LIDC-IDRI dataset and a modified multimodal Cityscapes
dataset. Results demonstrate that our method achieves the state-of-the-art or
competitive performance on all metrics.

该研究提出一种基于 MoSE 模型的分割不确定性计算方法，利用多个专家网络对图像进行不确定性判别，同时提出一种新颖的 Wasserstein-like 损失函数，可对模型进行有效训练和优化。研究表明，在 LIDC-IDRI 数据集和多模态 Cityscapes 数据集上，该方法表现出优越的性能。

使用混合随机专家建模分割中的多模态混沌不确定性

Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture  of Stochastic Experts

Unsupervised representation learning has proved to be a critical component of
anomaly detection/localization in images. The challenges to learn such a
representation are two-fold. Firstly, the sample size is not often large enough
to learn a rich generalizable representation through conventional techniques.
Secondly, while only normal samples are available at training, the learned
features should be discriminative of normal and anomalous samples. Here, we
propose to use the "distillation" of features at various layers of an expert
network, pre-trained on ImageNet, into a simpler cloner network to tackle both
issues. We detect and localize anomalies using the discrepancy between the
expert and cloner networks' intermediate activation values given the input
data. We show that considering multiple intermediate hints in distillation
leads to better exploiting the expert's knowledge and more distinctive
discrepancy compared to solely utilizing the last layer activation values.
Notably, previous methods either fail in precise anomaly localization or need
expensive region-based training. In contrast, with no need for any special or
intensive training procedure, we incorporate interpretability algorithms in our
novel framework for the localization of anomalous regions. Despite the striking
contrast between some test datasets and ImageNet, we achieve competitive or
significantly superior results compared to the SOTA methods on MNIST, F-MNIST,
CIFAR-10, MVTecAD, Retinal-OCT, and two Medical datasets on both anomaly
detection and localization.

本研究旨在通过将专家网络的特征进行 “蒸馏” 以学习无监督表示，并使用这些特征检测和定位异常区域，证明了结合多个中间提示的蒸馏方法能够更好地利用专家网络的知识，并显著提高在多个数据集上进行异常检测和定位的性能。