val-iisc/deligan
This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.
PythonMIT
Stargazers
- adam-hannaLos Angeles
- adrianalbertSLAC/MIT
- adzialochaBerlin
- akshitac8Toronto
- AnnaSingCommunication University of China
- anshulllIIT BHU (Varanasi)
- arjunkaruvallyUniversity of Massachusetts, Amherst
- ArunkumarRamananAI Founder @Deep-Brainz & Stealth AI Labs
- blaiszik
- bluemandora
- danrubinsBCM One
- DWUIDeveloper
- EvelynFanThe University of Hong Kong
- GenMori
- georgiazhang
- hangg7BAIR, UC Berkeley
- ihciah@ByteDance
- JihyongOhCMLab (cmlab.cau.ac.kr) @ CAU
- kilakila-heartxiamen fujian
- kinshuk4Pleo
- latticetower@kalininalab
- lovish1234
- MAGI003769University of Southern California
- Manik-GoyalVaranasi, India
- marcionicolauMN Estatística Consultoria & Pesquisa
- mehdidcJuelich Supercomputing Center (JSC), Forschungszentrum Jülich GmbH, LAION
- mrjohannchangTaipei
- sadanand-singhChicaho, IL
- Sampson-LeeSouth China University of Technology
- shepnerd@OpenGVLab
- Shiki-H
- unyqhz
- viveksck
- vkandolaGeolocation.getCurrentPosition()
- yongpi-scuSiChuan University
- zj199410