Matlab-GAN

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

• Facade Dataset
• Apple2Orange Dataset
• CelebA Dataset
• Mnist Dataset

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. 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)