Matlab-GAN

Versione 1.0.1 (213 MB) da Yui Chun Leung
MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN
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Aggiornato 22 mar 2023
Visualizza la licenza su 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.

Cita come

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

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)

Compatibilità della release di MATLAB
Creato con R2019b
Compatibile con R2019b fino a R2020a
Compatibilità della piattaforma
Windows macOS Linux

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AAE

ACGAN

CGAN

CycleGAN

DCGAN

GAN

InfoGAN

LSGAN

Pix2Pix

SGAN

WGAN

Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione Pubblicato Note della release
1.0.1

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1.0.0

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Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.