# Matlab-GAN **Repository Path**: h2hermit/Matlab-GAN ## Basic Information - **Project Name**: Matlab-GAN - **Description**: MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2020-05-10 - **Last Updated**: 2022-08-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Matlab-GAN [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![View Matlab-GAN on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/74865-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](https://github.com/eriklindernoren/Keras-GAN) and [PyTorch-GAN](https://github.com/eriklindernoren/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 - [Facade Dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/) - [Apple2Orange Dataset](http://www.image-net.org/) - [CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) - [Mnist Dataset](http://yann.lecun.com/exdb/mnist/) ## Table of Contents + **G**enerative **A**dversarial **N**etwork (GAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/GAN/GAN.m) [[paper]](https://arxiv.org/abs/1406.2661) + **L**east **S**quares **G**enerative **A**dversarial **N**etwork (LSGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/LSGAN/LSGAN.m) [[paper]](https://arxiv.org/abs/1611.04076) + **D**eep **C**onvolutional **G**enerative **A**dversarial **N**etwork (DCGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/DCGAN/DCGAN.m) [[paper]](https://arxiv.org/abs/1511.06434) + **C**onditional **G**enerative **A**dversarial **N**etwork (CGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/CGAN/CGAN.m) [[paper]](https://arxiv.org/abs/1611.06430) + **A**uxiliary **C**lassifier **G**enerative **A**dversarial **N**etwork (ACGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/ACGAN/ACGAN.m) [[paper]](https://arxiv.org/abs/1610.09585) + InfoGAN [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/InfoGAN/InfoGAN.m) [[paper]](https://arxiv.org/abs/1606.03657) + **A**dversarial **A**uto**E**ncoder (AAE) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/AAE/AAE.m) [[paper]](https://arxiv.org/abs/1511.05644) + Pix2Pix [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/Pix2Pix/PIX2PIX.m) [[paper]](https://arxiv.org/abs/1611.07004) + **W**asserstein **G**enerative **A**dversarial **N**etwork (WGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/WGAN/WGAN.m) [[paper]](https://arxiv.org/abs/1701.07875) + **S**emi-Supervised **G**enerative **A**dversarial **N**etwork (SGAN) [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/SGAN/SGAN.m) [[paper]](https://arxiv.org/abs/1606.01583) + CycleGAN [[code]](https://github.com/zcemycl/Matlab-GAN/blob/master/CycleGAN/CycleGAN.m) [[paper]](https://arxiv.org/abs/1703.10593) + DiscoGAN [[paper]](https://arxiv.org/abs/1703.05192) ## 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

DiscoGAN|SRGAN|| ## 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. Tyleček and R. Šára, “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, Martín 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)