DAM: Diffusion Activation Maximization for 3D Global Explanations
Hanxiao Tan
In recent years, the performance of point cloud models has been rapidly
improved. However, due to the limited amount of relevant explainability
studies, the unreliability and opacity of these black-box models may lead to
potential risks in applications where human lives are at stake, e.g. autonomous
driving or healthcare. This work proposes a DDPM-based point cloud global
explainability method (DAM) that leverages Point Diffusion Transformer (PDT), a
novel point-wise symmetric model, with dual-classifier guidance to generate
high-quality global explanations. In addition, an adapted path gradient
integration method for DAM is proposed, which not only provides a global
overview of the saliency maps for point cloud categories, but also sheds light
on how the attributions of the explanations vary during the generation process.
Extensive experiments indicate that our method outperforms existing ones in
terms of perceptibility, representativeness, and diversity, with a significant
reduction in generation time. Our code is available at:
https://github.com/Explain3D/DAM