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I'm not looking for a chunk of code as a solution, just the name of the model I'd need to implement or some links would be nice.

My problem is I have a dataset I've made of a few hundred 128x128 images (abstract paintings) - I'd like to simply generate more images similar to these images using a neural network (preferably no input needed for the network, except maybe random values?), but it's unclear as to how I'd go about this.

One solution I've thought about but haven't tried out yet is making an LSTM neural network, turning the paintings into 1D arrays of pixel values, and feeding the arrays to the network (LSTM networks are real good at learning sequences) - but if I'd want to work with larger images, this might not be very practical.

Any info is greatly appreciated. Thanks!

Robbie Barrat
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1 Answers1

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GANs (generative adversarial networks) would be appropriate in this case. GANs rely on two separate neural networks and, when properly trained, can be used to generate new images (a process known as hallucinating) that are similar to a collection of known images.

there are many examples of using GANs to generate new images of numbers from the canonical mnist dataset. naturally, you can replace mnist with your abstract paintings.

mynameisvinn
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