ML

The state of the art in image generation is BigGAN. Now, some trained models have been made available, including the capacity to interpolate between classes. I made a colab to easily create animations from these. They are pretty fun. What is more, they make it clear that the latent space clearly captures very meaningful shared properties across classes. The poses of quite different animals are conserved, and “cat eyes” clearly map onto “dog eyes” during interpolation. These sort of properties suggest that the network ‘understands’ the scene it is generating.

In the last post I introduced neural networks, generative adversarial networks (GANs) and InfoGANs. In this post I’ll describe the motivation and strategy for creating a GAN which generates images of biological cells, like this:

I recently began an AI Residency at Google, which I am enjoying a great deal. I have been experimenting with deep-learning approaches for a few years now, but am excited to immerse myself in this world over the coming year.