Jori Liesenborgs

DIVERSE: Disagreement-Inducing Vector Evolution for Rashomon Set Exploration

We propose DIVERSE, a framework for systematically exploring the Rashomon set of deep neural networks, the collection of models that match a reference model’s accuracy while differing in their predictive behavior. DIVERSE augments a pretrained model with Feature-wise Linear Modulation (FiLM) layers…