Pandaemonium Architecture 6.0

In this course

Arjovsky is a foundational figure in generative adversarial networks, credited as a key contributor to the GAN framework that underpins much of modern generative AI. His work appears in the course materials as part of the genealogy of adversarial training methods, which remain central to understanding how machines learn to produce synthetic data and images that challenge the boundary between real and artificial.

Mentioned in 2 readings

Andrew Brock

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

2

A Generative Adversarial Network (GAN) involves Generator (G) and Discriminator (D) networks whose purpose, respectively, is to map random noise to samples and discriminate real and generated samples. Formally, the GAN objective, in its ori…We explored a range of novel and existing techniques which ended up degrading or otherwise not affecting performance in our setting. We report them here; our evaluations for this section are not as thorough as those for the main architectur…

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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