Neural Synesthesia is an AI art project that aims to create new and unique audiovisual experiences with artificial intelligence. It does this through collaborations between humans and generative networks. The results feel almost like organic art. Swirls of color and images blend together as faces, scenery, objects, and architecture transform to music. There’s a sense of things swinging between feeling unique and at the same time oddly familiar.

Neural Synesthesia creator Xander Steenbrugge says "Most of the datasets I use are image sets I’ve encountered over the years. I saved them because I knew one day I’d have a use for them. I’ve always had an interest in aesthetics so when I discover something that triggers that sixth sense, I save it.

Most GAN papers use datasets of more than 50,000 images, but in practice you can get away with fewer examples. The first step is to start from a pre-trained GAN model that has already been trained on a large dataset. This means the convolutional filters in the model are already well-shaped and contain useful information about the visual world. Secondly, there’s data augmentation, which is basically flipping or rotating an image to effectively increase the amount of training data. Since I don’t really care about sample realism, I can actually afford to do very aggressive image augmentation. This results in many more training images than actual source images. For example, the model I used for a recent performance at Tate Modern had only 3,000 real images, aggressively augmented to a training set of around 70,000."

Via:[https://lionbridge.ai/articles/neural-synesthesia-when-art-meets-gans/]

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