Neural Additive Image Model: Interpretation through Interpolation

TL;DR

This paper introduces a neural additive model combined with diffusion autoencoders to interpret image effects and their influence on various outcomes, providing high flexibility and detailed understanding of image semantics.

Contribution

The paper presents a novel holistic modeling approach that effectively identifies complex image effects and their impact on real-world predictions, such as Airbnb rental pricing.

Findings

Accurately identifies complex image effects in ablation studies.

Demonstrates practical application in analyzing Airbnb rental prices.

Provides a flexible framework for exploring image characteristics' impact.

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 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.