Matlab-GAN

Versión 1.0.1 (213 MB) por Yui Chun Leung
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
4.8
(12)
2,4K Descargas
Actualizado 22 mar 2023
Ver licencia en GitHub

Collection of MATLAB implementations of Generative Adversarial Networks (GANs) suggested in research papers. It includes GAN, conditional-GAN, info-GAN, Adversarial AutoEncoder, Pix2Pix, CycleGAN and more, and the models are applied to different datasets such as MNIST, celebA and Facade.

Citar como

Yui Chun Leung (2024). Matlab-GAN (https://github.com/zcemycl/Matlab-GAN), GitHub. Recuperado .

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)

Compatibilidad con la versión de MATLAB
Se creó con R2019b
Compatible con cualquier versión desde R2019b hasta R2020a
Compatibilidad con las plataformas
Windows macOS Linux
Agradecimientos

Inspiración para: Advanced Techniques for 3D Model Generation in MATLAB

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

AAE

ACGAN

CGAN

CycleGAN

DCGAN

GAN

InfoGAN

LSGAN

Pix2Pix

SGAN

WGAN

No se pueden descargar versiones que utilicen la rama predeterminada de GitHub

Versión Publicado Notas de la versión
1.0.1

Add image
1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.