Pandaemonium Architecture 6.0

In this course

Karen Simonyan is a DeepMind researcher known for foundational work on generative image synthesis, particularly large-scale GANs for high-fidelity natural image generation. She appears here as a co-author of influential work on scaling generative models, which anchors the course's engagement with how machine learning produces synthetic visual content and what that means for the blurred boundaries between generation and authenticity. Her contributions represent the technical infrastructure underlying contemporary AI art and the aesthetic questions it raises about originality and representation.

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

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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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