Semi-supervised semantic segmentation allows model to mine effective
supervision from unlabeled data to complement label-guided training. Recent
research has primarily focused on consistency regularization techniques,
exploring perturbation-invariant training at both the image and feature levels.
In this work, we proposed a novel feature-level consistency learning framework
named Density-Descending Feature Perturbation (DDFP). Inspired by the
low-density separation assumption in semi-supervised learning, our key insight
is that feature density can shed a light on the most promising direction for
the segmentation classifier to explore, which is the regions with lower
density. We propose to shift features with confident predictions towards
lower-density regions by perturbation injection. The perturbed features are
then supervised by the predictions on the original features, thereby compelling
the classifier to explore less dense regions to effectively regularize the
decision boundary. Central to our method is the estimation of feature density.
To this end, we introduce a lightweight density estimator based on normalizing
flow, allowing for efficient capture of the feature density distribution in an
online manner. By extracting gradients from the density estimator, we can
determine the direction towards less dense regions for each feature. The
proposed DDFP outperforms other designs on feature-level perturbations and
shows state of the art performances on both Pascal VOC and Cityscapes dataset
under various partition protocols. The project is available at
this https URL

基于密度下降的特征扰动（DDFP）是一种新颖的特征级一致性学习框架，旨在通过扰动注入将特征移向密度较低的区域，有效地规范决策边界，并通过轻量级密度估计器实时捕获特征密度分布，该方法在特征级扰动上胜过其他设计并在 Pascal VOC 和 Cityscapes 数据集上展现出卓越的性能。

朝未知领域进发：密度递减特征扰动用于半监督语义分割

Towards the Uncharted: Density-Descending Feature Perturbation for  Semi-supervised Semantic Segmentation

Out-of-distribution (OOD) detection is a critical task for safe deployment of
learning systems in the open world setting. In this work, we investigate the
use of feature density estimation via normalizing flows for OOD detection and
present a fully unsupervised approach which requires no exposure to OOD data,
avoiding researcher bias in OOD sample selection. This is a post-hoc method
which can be applied to any pretrained model, and involves training a
lightweight auxiliary normalizing flow model to perform the out-of-distribution
detection via density thresholding. Experiments on OOD detection in image
classification show strong results for far-OOD data detection with only a
single epoch of flow training, including 98.2% AUROC for ImageNet-1k vs.
Textures, which exceeds the state of the art by 7.8%. We additionally explore
the connection between the feature space distribution of the pretrained model
and the performance of our method. Finally, we provide insights into training
pitfalls that have plagued normalizing flows for use in OOD detection.

基于特征密度估计和正态流的无监督型外分布检测方法可应用于任意预训练模型，通过密度阈值实现检测，并在图像分类中表现出强大的远外分布数据检测结果，包括 ImageNet-1k 与纹理的 98.2% AUROC，超过现有技术 7.8%。我们还探讨了预训练模型的特征空间分布与我们方法性能之间的关联，并提供了应用正态流用于外分布检测时的训练陷阱。