Pandaemonium Architecture 6.0

In this course

Andrew Brock is a machine learning researcher whose work on large-scale generative adversarial networks (GANs) for high-fidelity image synthesis represents a key milestone in making AI image generation practical and scalable. His research introduces techniques for navigating the quality-diversity trade-off in generative models—a fundamental tension in how machines learn to produce convincing synthetic images. The course readings invoke his work to ground discussions of how contemporary generative systems balance fidelity against variety, a problem central to understanding both the technical capabilities and aesthetic implications of AI-generated imagery.

Mentioned in 2 readings

Andrew Brock

et al., LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS

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Large Scale GAN Training for High Fidelity Natural Image Synthesis Andrew Brock\† Heriot-Watt University ajb5@hw.ac.uk Jeff Donahue† DeepMind jeffdonahue@google.com Karen Simonyan† DeepMind simonyan@google.com Abstract Despite recent p…This technique allows fine-grained, post-hoc selection of the trade-off between sample quality and variety for a given G. Notably, we can compute FID and IS for a range of thresholds, obtaining the variety-fidelity curve reminiscent of the …

Robin Rombach

et al., High-Resolution Image Synthesis with Latent Diffusion Models

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6. Conclusion We have presented latent diffusion models, a simple and efficient way to significantly improve both the training and sampling efficiency of denoising diffusion models without degrading their quality. Based on this and our cro…

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