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AI grid machine learning GANs shoes

Sneaker design using AI

Why not let AI design sneakers for your next shoe collection?

Stan Van Der Vossen used so-called Generative Adversarial Networks in the development of, a site that generates an infinite variety of shapes and forms in sneaker design – all based on a dataset fed with Instagram images of existing shoe designs. The interface even lets the user tweak designs based on a set number of parameters.

In all its playfulness, the technology displays how Artificial Intelligence potentially could be used as a tool, or even direct the design process for fashion designers in the future.

A kind warning. Don’t fall in love with any of the designs. They do not exist.



What are GANs and how do they work?

At a high level, a GAN is a neural network that uses training data to generate realistic examples. For example, GANs use photos of handwritten digits to learn how to create more real photos of handwritten digits. Even more impressively, GANs can even learn to create photorealistic photos of faces.

In healthcare, GANs have been shown to be very effective in generating images for medical image analysis. In particular, GANs have been used to create realistic images of organs for surgical planning or simulation training. For example, samples generated from tumors can be used for diagnosis and treatment planning. Or a dataset of skin moles can enable identification of skin cancer by uploading a few camera pictures.

GANs introduce the concept of adversarial learning as they exist in the competition between two neural networks. These techniques allow researchers to create realistic but entirely computer-generated photographs of human faces. They also allow the creation of controversial “deepfake” videos. In fact, GANs can be used to model any data distribution (images, text, sounds, etc.).