PyTorch implementation of Generative Adversarial Networks and play around with CelebA dataset, github-like Identicon images and Pokemon sprints.
Intermediate Results of Identicon Generation
├── bin
│ ├── train.py
│ ├── dashboard.ipynb
│ ├── session_1
│ ├── session_2
│ ├── ...
│ └── session_n
├── core
│ ├── dataloader
│ │ ├── transforms
│ │ └── utils
│ ├── engine
│ │ ├── config_file.py
│ │ ├── trainer.py
│ │ └── utils
│ └── models
│ ├── backbones
│ └── modules
├── data
├── docs
└── utils
data
: contains actual data i.e. CelebA faces and Pokemon sprints, structured according to provider conventionsdocs
: any paper, notes, figures relevant to this repositorybin
:dashboard.ipynb
is our UI to setup experience sessions, setting up an associated directorysession_i
. The experiment is then launched through executingtrain.py
, here for more detailscore
: contains definition of data processing and loading protocols, models and training engines
@incollection{NIPS2014_5423,
title = {Generative Adversarial Nets},
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
booktitle = {Advances in Neural Information Processing Systems 27},
editor = {Z. Ghahramani and M. Welling and C. Cortes and N. D. Lawrence and K. Q. Weinberger},
pages = {2672--2680},
year = {2014},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf}
}