https://github.com/themattinthehatt/dreamscape
Playing around with generative models trained on natural images, along with (eventually) some visualization tools.
https://github.com/themattinthehatt/dreamscape
gan generative-model natural-images tensorflow vae variational-autoencoder
Last synced: 12 months ago
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Playing around with generative models trained on natural images, along with (eventually) some visualization tools.
- Host: GitHub
- URL: https://github.com/themattinthehatt/dreamscape
- Owner: themattinthehatt
- License: mit
- Created: 2016-12-15T17:57:18.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-06-18T01:10:02.000Z (almost 9 years ago)
- Last Synced: 2025-03-22T08:11:10.321Z (about 1 year ago)
- Topics: gan, generative-model, natural-images, tensorflow, vae, variational-autoencoder
- Language: Python
- Size: 668 KB
- Stars: 5
- Watchers: 3
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# dreamscape
Playing around with generative models trained on natural images, along with (eventually) some visualization tools.
## Variational Autoencoder (VAE)
Resources:
* original vae paper I - [Kingma and Welling 2014](https://arxiv.org/abs/1312.6114)
* original vae paper II - [Rezende, Mohamed and Wierstra 2014](https://arxiv.org/abs/1401.4082)
* tutorial paper - [Doersch 2016](https://arxiv.org/pdf/1606.05908v2.pdf)
* helpful blog - [Variational Autoencoder in Tensorflow, Jan Hendrik Metzen](https://jmetzen.github.io/2015-11-27/vae.html)
* conditional vae paper - [Sohn, Lee and Yan 2015](https://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models)
## Generative Adversarial Network (GAN)
Resources:
* original gan paper - [Goodfellow et al. 2014](https://arxiv.org/abs/1406.2661)
* tutorial paper - [Goodfellow 2017](https://arxiv.org/pdf/1701.00160.pdf)
* dcgan paper - [Radford et al. 2016](https://arxiv.org/abs/1511.06434)