# Tensorflow Implementation of OCGAN This repository provides a [Tensorflow](https://www.tensorflow.org/) implementation of the OCGAN presented in CVPR 2019 paper “[OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations](http://openaccess.thecvf.com/content_CVPR_2019/papers/Perera_OCGAN_One-Class_Novelty_Detection_Using_GANs_With_Constrained_Latent_Representations_CVPR_2019_paper.pdf)”.
The author’s implementation of OCGAN in MXNet is at [here](https://github.com/PramuPerera/OCGAN).
## 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: `bash pip install ocgan `
and
`python from ocgan import OCGAN `
Clone this repository:
`bash git clone https://github.com/nuclearboy95/Deep-SVDD-Tensorflow.git `
Metadata
Release files for ocgan 1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ocgan-1.0-py3-none-any.whl | Python 3 | none | any | Details for ocgan-1.0-py3-none-any.whl |
Release files / ocgan-1.0-py3-none-any.whl
| Download URL | ocgan-1.0-py3-none-any.whl |
|---|---|
| Size | 6.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
bf5c9aa0f8c3a978ebecf42f4da1fa2e72509b3562e4759939b4db6526ad1d7e
|
|
BLAKE2b-256 checksum How to use checksums |
0b9c5ff721d4418166bd1a893fee3e3dc887f544d5dd90f3bb9fdb1020a7c366
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.3.2 requests/2.14.2 setuptools/41.4.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.5.2
|
Security for ocgan 1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.