# Noise-guided Unsupervised Outlier Detection

Here we provide the implementation for the paper **Noise-guided Unsupervised Outlier Detection**.

"code" folder is the complete implementation for it.

In `data` folder, `model_x.npy` is the artificially synthesized two-dimensional data feature, and `model_x.npy` is the label that contains 160 inliers, 40 outliers and 168 noise samples. 

# MP4 explanation

For the file 'change_pred_value_in_breastw.mp4', this is the trend graph of the model prediction values of inliers, outliers and noise with the training process during NOD training 500 epochs on the **breastw** real-world dataset. The red dots in the figure represent inliers, the blue dots represent outliers, and the green dots represent noise from negative sampling.

It can be clearly seen from the figure that the distance between inliers and outliers will be continuously pulled apart due to the addition of noise as the training progresses. Once the abnormal ratio is given, we can distinguish outliers from inliers well.