CDT Pizza 30/11
Talk #1
Speaker
Andrew Brock
Title
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
Abstract
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. In this talk, I'll give an overview of my internship work at DeepMind, training Generative Adversarial Networks at the largest scale yet attempted, and studying the instabilities specific to such scale. The modifications we propose lead to a 200-300% improvement in the state of the art in conditional image synthesis (as measured by Inception Score and FID), and allow us to finely control the tradeoff between sample quality and diversity.
Talk #2
Speaker
Alan Bundy
Title
Darpa's Third Wave of AI
Abstract
The US funding agency DARPA is investing $2Bn in a research programme to combine the best of symbolic and sub-symbolic AI. It's slogan is that "Systems [will] construct contextual explanatory models for classes of real world phenomena". It divides the history of AI into 3 waves: the first wave was symbolic AI, e.g., expert systems; the 2nd wave is statistical machine learning; and the 3rd wave will combine these. Edinburgh has strengths in the first two waves. We should examine whether we can profitably join forces.