CodingTrain / CodingTrain/Suggestion-Box
GAN - Generative Adversarial Networks and creative generative art
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Description
Hi Dan,
Here is a link to a video about "Progressive Growing of GANs for Improved Quality, Stability, and Variation". I think it captures the amazing things you can do with neural networks.
[Demonstration of GANs](https://www.youtube.com/watch?v=XOxxPcy5Gr4)
Also, there are many videos online on "deep dreams" [An example](https://www.youtube.com/watch?v=SCE-QeDfXtA)
All of these videos were created with GANs\Generative Adversarial Networks. These are a class of neural networks, mainly in unsupervised learning. [Wikipedia Link](https://en.wikipedia.org/wiki/Generative_adversarial_network)
They were used for the [Pix2Pix model](https://affinelayer.com/pixsrv/) also see [Pix2Pix Article](https://arxiv.org/abs/1611.07004).
Sentdex has recently done a series about unusual neural networks, where he covered GANs. There is also a Computerphile video on this subject. These videos are amazing about learning how GANs work, but they don't cover their inner workings. Also, I haven't seen anyone on YouTube talk about the cool art that can be made with GANs, and the creative coding opportunities.
Maybe it would be interesting to have a mini-series about GANs, something like "Generative Adversarial Networks for Artists". Starting with how GANs really work and then moving on to TensorFlow.js and [the hvass-labs tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb) and finally have community contributions and showcase community art.
I know you'll be able to make this interesting and easy to understand.
Hope you like my idea. Best regards,
Eitan
Contributor guide
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Research direction
Start by reviewing the linked Progressive Growing of GANs, Pix2Pix, TensorFlow.js, and hvass-labs DeepDream resources. Define a scoped mini-series covering GAN fundamentals, creative coding, TensorFlow.js, the referenced tutorial, and community art contributions; done means the series scope and contribution path are agreed.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100