一种大气急流计算方法

异常检测是计算机视觉的一个经典问题.针对异常样本稀少在真实场景中异常很难被捕捉，且标签难以获取，提出一种仅用正常样本进行训练的端到端异常检测模型.首先，通过自动编码器对输入图像进行编码，得到它的低维特征；然后，用一个自回归概率密度估计器对低维特征的概率分布进行正则约束，解码器再将其恢复至原始输入大小；最后，使用一个分类器来判断生成图片的真假.编解码器之间使用了跳线连接，能够最大限度地提高该模型对正常样本的记忆能力.本文在CIFAR 10和UCSD Ped2数据集上进行了实验，测试结果显示，IFAR 10总共10个类别的平均曲线下面积（AUC）达到73.5%，UCSD Ped2的平均曲线下面积（AUC）达到95.7%.结果证明，该模型能够明显提高异常检测的效果.

An anomaly detection method based on feature regular constraints

Anomaly detection is a classic problem in computer vision. It is difficult to capture the anomalies in the real scene and is difficult to obtain the labels as well, an end to end anomaly detection model trained only with normal samples is proposed. First, the input image is encoded by an automatic encoder to obtain its low dimensional features, and then an autoregressive probability density estimator is used to constrain the probability distribution of low dimensional features. The decoder restores it to the original input size. Finally, a classifier determines the authenticity of the generated picture. A jumper connection is used between the codecs to maximize the memory of the model for normal samples. In this paper, the experiments were conducted on the CIFAR 10 and UCSD Ped2 datasets. The results showed that the average area under the curve (AUC) of the 10 categories of CIFAR10 reached 73.5%, and the area under the average curve (AUC) of UCSDPed2 reached 95.7%. This model can effectively improve the effect of anomaly detection.