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…With the default suggested $\gamma$ strength of 10, training becomes stable and improves the smoothness and boundedness of spectra in both G and D, but performance severely degrades, resulting in a 45% reduction in IS. Reducing the penalty …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…
Ishaan Gulrajani
scientist · 3 mentions across 1 reading
In this course
Gulrajani is a machine learning researcher known for work on stabilizing and improving generative adversarial networks, particularly through techniques like gradient penalties that address training instability in GANs. The course readings cite his methods for tuning discriminator-generator dynamics, using him to ground practical questions about the trade-offs between training stability and model performance. His work appears here as an example of the engineering challenges underlying generative systems that the seminar examines as both technical problems and sites of aesthetic possibility.
Mentioned in 1 reading
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