moments-vae

This repo tries to do outlier detection with the Sum of Squares polynomial Q(x).

Some details about the code:

There are basically 2 approaches:

1. Use empirical Moment Matrix, which is implemented in:

• class Q_Real_M
• class Q_Real_M_Batches (still in maintenance)

2. Learn directly , which is implemented in:

• class Q
• class Q_MyBilinear
• class Q_PSD

Q and Q_MyBilinear do the same, is just that one uses a pytorch built-in function, the other not (I did it because I thought there was a bug)

USAGE

python train_forwen.py --model Q_real_M --writer experimentname --idx_inliers 1 --device 2

Requirements

Most of them are not strictly necessary, but I paste the pip freeze of the environment I was using :)

absl-py==0.8.1 astor==0.8.0 certifi==2019.9.11 chardet==3.0.4 cycler==0.10.0 decorator==4.3.0 gast==0.2.2 google-pasta==0.1.7 grpcio==1.24.1 h5py==2.10.0 idna==2.8 imageio==2.6.1 imageio-ffmpeg==0.3.0 Keras-Applications==1.0.8 Keras-Preprocessing==1.1.0 kiwisolver==1.0.1 Markdown==3.1.1 matplotlib==3.0.2 moviepy==1.0.1 networkx==2.2 numpy==1.17.2 opencv-python==3.4.3.18 opt-einsum==3.1.0 Pillow==5.3.0 pkg-resources==0.0.0 prettytable==0.7.2 proglog==0.1.9 protobuf==3.10.0 pyparsing==2.3.0 python-dateutil==2.7.5 PyWavelets==1.0.1 requests==2.22.0 scikit-image==0.16.1 scikit-learn==0.19.1 scipy==1.1.0 six==1.11.0 tensorboard==2.0.0 tensorboardX==1.9 tensorflow==2.0.0 tensorflow-estimator==2.0.0 termcolor==1.1.0 torch==0.4.1 torchvision==0.2.1 tqdm==4.28.1 urllib3==1.25.6 Werkzeug==0.16.0 wrapt==1.11.2