1 citations · 1 across the 2 of their papers we have counts for
3 papers
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…
Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation
Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen +1
Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality.…