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Everyone who liked this should like to read on Generative Adversarial (Neural) Networks, generative models where a pair of neural networks are trained one agai
by make3 8y ago
Everyone who liked this should like to read on Generative Adversarial (Neural) Networks, generative models where a pair of neural networks are trained one against the other to learn to generate new images. This has been put to many great purposes, including artistic ones: https://towardsdatascience.com/gangogh-creating-art-with-gans-8d087d8f74a1 https://towardsdatascience.com/gangogh-creating-art-with-gan...
- weego 8y agoNo it's a completely different interest and skillet. Not everything can be boiled down to "datascience can fix it!". Especially when all those NNs will just generate art that's a mashup of the training data and won't have any real inspiration. Unexpected results is not inspiration.
- deleted 8y ago[deleted]
- yorwba 8y agoUsing GANs for art is not necessarily limited to training on a collection of famous artists to replicate their style. Some artists use them as another tool to process and transform their artwork https://thegradient.pub/playing-a-game-of-ganstruction/ https://thegradient.pub/playing-a-game-of-ganstruction/ I'm pretty sure that the invention of photography was also greeted with complaints that it lacked "real inspiration" because a photographer "just" had to choose what to take a picture of. But the real inspiration is always in what you choose your art to be, and not whether you create it by applying paint to a canvas.