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Loss Functions in Diffusion Models: A Comparative Study
Dibyanshu Kumar, Philipp Vaeth, Magda Gregorová
Diffusion models have emerged as powerful generative models, inspiring extensive research into their underlying mechanisms. One of the key questions in this area is the loss functi…
Diffusion Classifier Guidance for Non-robust Classifiers
Philipp Vaeth, Dibyanshu Kumar, Benjamin Paassen +1
Classifier guidance is intended to steer a diffusion process such that a given classifier reliably recognizes the generated data point as a certain class. However, most classifier…
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen +1
Recently, several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers. Despite producing visually stunning results, these met…
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling
Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen +1
To sample from an unconditionally trained Denoising Diffusion Probabilistic Model (DDPM), classifier guidance adds conditional information during sampling, but the gradients from c…