matlab-gan
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.5%) to scientific vocabulary
Keywords
Repository
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
Basic Info
Statistics
- Stars: 204
- Watchers: 8
- Forks: 86
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
Matlab-GAN 
Collection of MATLAB implementations of Generative Adversarial Networks (GANs) suggested in research papers. This repository is greatly inspired by eriklindernoren's repositories Keras-GAN and PyTorch-GAN, and contains codes to investigate different architectures of GAN models.
Configuration
To run the following codes, users should have the following packages, - MATLAB 2019b - Deep Learning Toolbox - Parallel Computing Toolbox (optional for GPU usage)
Datasets
Table of Contents
- Generative Adversarial Network (GAN) [code] [paper]
- Least Squares Generative Adversarial Network (LSGAN) [code] [paper]
- Deep Convolutional Generative Adversarial Network (DCGAN) [code] [paper]
- Conditional Generative Adversarial Network (CGAN) [code] [paper]
- Auxiliary Classifier Generative Adversarial Network (ACGAN) [code] [paper]
- InfoGAN [code] [paper]
- Adversarial AutoEncoder (AAE) [code] [paper]
- Pix2Pix [code] [paper]
- Wasserstein Generative Adversarial Network (WGAN) [code] [paper]
- Semi-Supervised Generative Adversarial Network (SGAN) [code] [paper]
- CycleGAN [code] [paper]
- DiscoGAN [paper]
Outputs
GAN
-Generator, Discriminator| LSGAN
-Least Squares Loss | DCGAN
-Deep Convolutional Layer | CGAN
-Condition Embedding
:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:
|
|
|
ACGAN
-Classification|InfoGAN mnist
-Continuous, Discrete Codes|AAE
-Encoder, Decoder, Discriminator|Pix2Pix
-Pair and Segments checking
-Decovolution and Skip Connections
|
|
|
WGAN |SGAN|CycleGAN
-Instance Normalization
-Mutli-agent Learning|InfoGAN CelebA
|
|
|
References
- Y. LeCun and C. Cortes, MNIST handwritten digitdatabase, 2010. [MNIST]
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, andL. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database, inCVPR09, 2009. [Apple2Orange (ImageNet)]
- R. Tyleek and R. ra, Spatial pattern templates forrecognition of objects with regular structure, inProc.GCPR, (Saarbrucken, Germany), 2013. [Facade]
- Z. Liu, P. Luo, X. Wang, and X. Tang, Deep learn-ing face attributes in the wild, inProceedings of In-ternational Conference on Computer Vision (ICCV),December 2015. [CelebA]
- Goodfellow, Ian J. et al. Generative Adversarial Networks. ArXiv abs/1406.2661 (2014): n. pag. (GAN)
- Radford, Alec et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. CoRR abs/1511.06434 (2015): n. pag. (DCGAN)
- Denton, Emily L. et al. Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks. ArXiv abs/1611.06430 (2017): n. pag. (CGAN)
- Odena, Augustus et al. Conditional Image Synthesis with Auxiliary Classifier GANs. ICML (2016). (ACGAN)
- Chen, Xi et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS (2016). (InfoGAN)
- Makhzani, Alireza et al. Adversarial Autoencoders. ArXiv abs/1511.05644 (2015): n. pag. (AAE)
- Isola, Phillip et al. Image-to-Image Translation with Conditional Adversarial Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016): 5967-5976. (Pix2Pix)
- J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpairedimage-to-image translation using cycle-consistent ad-versarial networks, 2017. (CycleGAN)
- Arjovsky, Martn et al. Wasserstein GAN. ArXiv abs/1701.07875 (2017): n. pag. (WGAN)
- Odena, Augustus. Semi-Supervised Learning with Generative Adversarial Networks. ArXiv abs/1606.01583 (2016): n. pag. (SGAN)
Owner
- Name: Leo Leung
- Login: zcemycl
- Kind: user
- Location: London
- Website: zcemycl.github.io
- Repositories: 141
- Profile: https://github.com/zcemycl
冒険の物語 はじまりは今日でいい いたましいエレジー 誰も謳わぬように prophecy
GitHub Events
Total
- Watch event: 19
- Fork event: 2
Last Year
- Watch event: 19
- Fork event: 2
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 15
- Total pull requests: 0
- Average time to close issues: 25 days
- Average time to close pull requests: N/A
- Total issue authors: 10
- Total pull request authors: 0
- Average comments per issue: 1.67
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- momo-love (3)
- hurmaeht (3)
- NicoMigenda (2)
- chang7ing (1)
- abhilasha2614 (1)
- tjr1 (1)
- ApplePomme (1)
- prymhdv (1)
- jlherraiz (1)
- songport (1)