Benchmarking Counterfactual Image Generation

Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez, Athanasios Vlontzos, Yannis Panagakis, Giorgos Papanastasiou, Sotirios A. Tsaftaris

Keywords: Vision and Learning

TL;DR: We introduce a unified framework to benchmark counterfactual image generation methods, showing that Hierarchical VAEs outperform alternatives across diverse datasets and causal structures.

Abstract:

Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data generation process. Such image editing falls into the counterfactual image generation regime. Evaluating counterfactual image generation is substantially complex: not only it lacks observable ground truths, but also requires adherence to causal constraints. Although several counterfactual image generation methods and evaluation metrics exist, a comprehensive comparison within a unified setting is lacking. We present a comparison framework to thoroughly benchmark counterfactual image generation methods. We integrate all models that have been used for the task at hand and expand them to novel datasets and causal graphs, demonstrating the superiority of Hierarchical VAEs across most datasets and metrics. Our framework is implemented in a user-friendly Python package that can be extended to incorporate additional SCMs, causal methods, generative models, and datasets for the community to build on.

Vision and Learning
https://proceedings.neurips.cc/paper_files/paper/2024/hash/f04351c9fa1e22797c7d32c1f6d23948-Abstract-Datasets_and_Benchmarks_Track.html

Submission Number: 22