An image-computable model of human visual shape similarity
Fig 3
GANs produce novel naturalistic shapes.
(A) Cartoon depiction of a Generative Adversarial Networks (GANs) that synthesizes novel shape silhouettes. GANs are unsupervised machine learning systems with two competing neural networks. The generator network synthesizes shapes, while the discriminator network, distinguishes shapes produced by generator from a database of over 25,000 animal silhouettes. With training, the generator learns to map a high-dimensional latent vector ‘z’ to the natural animal shapes, producing novel shapes that the discrimantor thinks are real rather than synthesized. Systematically moving along the high-dimensional latent vector z produces novel shape variation and interpolations across a shape space (B, C, and D). (E) A normalized histogram with the number of unique responses across 100 GAN shapes and 20 animal shapes shows that category responses across GAN shapes tend to be much more inconsistent across participants than animal shapes, confirming that GAN shapes appear more unfamiliar than animal shapes.