Jens Rahnfeld

Scalable Perturbation-Based Explanations via Tradeoff-Conditioned Smooth Masking

This work introduces a sampling-free, perturbation-based training framework based on continuous and differentiable masking that achieves competitive or superior attribution faithfulness compared to strong sampling-based baselines, while dramatically reducing computational cost and enabling substantially improved scalab...