Neural Additive Image Model: Interpretation through Interpolation

arXiv:2405.02295v1 Announce Type: cross
Abstract: Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely interesting problems. By adopting a holistic modeling approach utilizing Neural Additive Models in combination with Diffusion Autoencoders, we can effectively identify the latent hidden semantics of image effects and achieve full intelligibility of additional tabular effects. Our approach offers a high degree of flexibility, empowering us …

abstract arxiv autoencoders combination cs.cv cs.lg diffusion effects image images influence interpolation interpretation modeling predictions semantics through type understanding world