# Insertion and deletion curves

Attribution maps for explainable AI were previously produced by heuristics, with faithfulness only measured afterward by insertion and deletion curves. Ra-NEM turns the area under those curves into a differentiable objective, using the Gumbel top-k trick for differentiable top-k feature selection inside the neural explanation mask framework. On ImageNet-pretrained ResNet50, ConvNeXt, VGG16 and ViT it reaches the highest average faithfulness and the fastest inference, though it ranks fifth on VGG16.

Attribution maps for explainable AI were previously produced by heuristics, with faithfulness only measured afterward by insertion and deletion curves. Ra-NEM turns the area under those curves into a differentiable objective, using the Gumbel top-k trick for differentiable top-k feature selection inside the neural explanation mask framework. On ImageNet-pretrained ResNet50, ConvNeXt, VGG16 and ViT it reaches the highest average faithfulness and the fastest inference, though it ranks fifth on VGG16.