2 citations · 4 across the 8 of their papers we have counts for
10 papers · 1 filter
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…
Discrete Graph Auto-Encoder
Yoann Boget, Magda Gregorova, Alexandros Kalousis
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique…
GrannGAN: Graph annotation generative adversarial networks
Yoann Boget, Magda Gregorova, Alexandros Kalousis
We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. Th…
Learned transform compression with optimized entropy encoding
Magda Gregorová, Marc Desaules, Alexandros Kalousis
We consider the problem of learned transform compression where we learn both, the transform as well as the probability distribution over the discrete codes. We utilize a soft relax…
Improving VAE generations of multimodal data through data-dependent conditional priors
Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. Thi…