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nuclearboy95 / Anomaly-Detection-OCGAN-tensorflow

Tensorflow implementation of OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
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Tensorflow Implementation of OCGAN

This repository provides a Tensorflow implementation of the OCGAN presented in CVPR 2019 paper "OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations".

The author's implementation of OCGAN in MXNet is at here.

Installation

This code is written in Python 3.5 and tested with Tensorflow 1.13.

Install using pip or clone this repository.

  1. Installation using pip: 
    pip install ocgan

and

from ocgan import OCGAN
  1. Clone this repository:
git clone https://github.com/nuclearboy95/Anomaly-Detection-OCGAN-tensorflow.git

Result (AUROC)

MNIST DIGIT OCGAN w/
Informative-negative
mining		 OCGAN w/o
Informative-negative
mining
0 0.9952 0.9935
1 0.9976 0.9985
2 0.9268 0.9133
3 0.9410 0.9208
4 0.9636 0.9600
5 0.9613 0.9145
6 0.9910 0.9835
7 0.9658 0.9526
8 0.9009 0.8758
9 0.9584 0.9701

NOTE: The AUROC values are measured only once for each digit.