mlsanityData to 14 Oct 2026

Neural Additive Image Model: Interpretation through Interpolation

arXiv 2405.02295 ↗First posted 6 Mar 2024cs.CV cs.LGcited by 1

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Abstract (arXiv)

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 to comprehensively explore the impact of various image characteristics. We demonstrate that the proposed method can precisely identify complex image effects in an ablation study. To further showcase the practical applicability of our proposed model, we conduct a case study in which we investigate how the distinctive features and attributes captured within host images exert influence on the pricing of Airbnb rentals.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 45 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

33 of 45 references.

References · 33

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21 more
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  1. 2025Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networkscited by 2