Pandaemonium Architecture 6.0

In this course

Simonyan is a DeepMind researcher known for work on generative image synthesis and deep learning architectures who co-authored foundational papers on GANs and diffusion models for high-fidelity image generation. Her research appears in the course readings as part of the technical lineage enabling contemporary AI image synthesis—specifically cited alongside advances in latent diffusion and feature-space analysis that underpin modern generative systems. This positions her work as crucial infrastructure for understanding how contemporary image-generating AI systems function within the landscape of computational creativity and machine learning.

Mentioned in 2 readings

Andrew Brock

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

2

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…Published as a conference paper at ICLR 2019 Caption: Figure 11: Nearest neighbors in ResNet-50-avgpool (He et al., 2016) feature space. The generated image is in the top left. Caption: Figure 12: Nearest neighbors in pixel space. The gener…

Robin Rombach

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

1

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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