vaegan VS awesome-generative-modeling

Compare vaegan vs awesome-generative-modeling and see what are their differences.

vaegan

An implementation of VAEGAN (variational autoencoder + generative adversarial network). (by anitan0925)

awesome-generative-modeling

Bolei's archive on generative modeling (by zhoubolei)
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured
vaegan awesome-generative-modeling
1 1
91 159
- -
10.0 10.0
about 7 years ago about 3 years ago
Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

vaegan

Posts with mentions or reviews of vaegan. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-18.

awesome-generative-modeling

Posts with mentions or reviews of awesome-generative-modeling. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-18.

What are some alternatives?

When comparing vaegan and awesome-generative-modeling you can also consider the following projects:

lightweight-gan - Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

GlowIP - Code to reproduce results from "Invertible generative models for inverse problems: mitigating representation error and dataset bias"