We present the efficiency of semi-orthogonal embedding for unsupervised
anomaly segmentation. The multi-scale features from pre-trained CNNs are
recently used for the localized Mahalanobis distances with significant
performance. However, the increased feature size is problematic to scale up to
the bigger CNNs, since it requires the batch-inverse of multi-dimensional
covariance tensor. Here, we generalize an ad-hoc method, random feature
selection, into semi-orthogonal embedding for robust approximation, cubically
reducing the computational cost for the inverse of multi-dimensional covariance
tensor. With the scrutiny of ablation studies, the proposed method achieves a
new state-of-the-art with significant margins for the MVTec AD, KolektorSDD,
KolektorSDD2, and mSTC datasets. The theoretical and empirical analyses offer
insights and verification of our straightforward yet cost-effective approach.

本文介绍了一种半正交嵌入模型，用于无监督异常分割任务。该模型使用预训练卷积神经网络中的多尺度特征来计算局部马氏距离，使用随机特征选择的方式对多维协方差张量进行逼近，从而极大地降低了计算开销，实验结果表明该方法在多个数据集上表现显著优于现有最优解。

半正交嵌入用于高效无监督异常分割

Semi-orthogonal Embedding for Efficient Unsupervised Anomaly  Segmentation

Current methods for video analysis often extract frame-level features using
pre-trained convolutional neural networks (CNNs). Such features are then
aggregated over time e.g., by simple temporal averaging or more sophisticated
recurrent neural networks such as long short-term memory (LSTM) or gated
recurrent units (GRU). In this work we revise existing video representations
and study alternative methods for temporal aggregation. We first explore
clustering-based aggregation layers and propose a two-stream architecture
aggregating audio and visual features. We then introduce a learnable non-linear
unit, named Context Gating, aiming to model interdependencies among network
activations. Our experimental results show the advantage of both improvements
for the task of video classification. In particular, we evaluate our method on
the large-scale multi-modal Youtube-8M v2 dataset and outperform all other
methods in the Youtube 8M Large-Scale Video Understanding challenge.

通过聚类聚合和可学习的非线性单元 Context Gating 模型改进了现有视频表达方式，提高了视频分类准确率。

使用上下文门控的可学习池化方法进行视频分类

Learnable pooling with Context Gating for video classification

Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of research. In this work, we present an intriguing behavior: pre-trained
CNNs can be made to improve their predictions by structurally perturbing the
input. We observe that these perturbations - referred as Guided Perturbations -
enable a trained network to improve its prediction performance without any
learning or change in network weights. We perform various ablative experiments
to understand how these perturbations affect the local context and feature
representations. Furthermore, we demonstrate that this idea can improve
performance of several existing approaches on semantic segmentation and scene
labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
MNIST and CIFAR10 datasets.

本篇论文探究了卷积神经网络中的一种新行为：通过结构化扰动输入，预训练的 CNN 可以改善其预测性能，这种扰动被称为 Guided Perturbations。实验结果表明，这种方法可以提高现有方法在语义分割和场景标注任务上的性能，并且可以应用于 MNIST 和 CIFAR10 等数据集上的分类任务。