SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models

2026-03-17

AI Summary

This work proposes a novel approach to visual counterfactual explanations by leveraging general-purpose pretrained generative foundation models, circumventing the need for task-specific generative architectures that are computationally expensive and difficult to scale to high-resolution data. By operating in the latent space of these foundation models, the method integrates smooth gradient-based optimization with a mask-guided diversity strategy to efficiently produce high-fidelity, structurally diverse counterfactual samples without any additional training. Evaluated on both natural images and medical imaging datasets, the approach achieves explanation quality comparable to or better than existing methods while substantially reducing computational and training overhead, thereby enhancing scalability and practical applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled approach to interpreting black-box predictions by identifying minimal input changes that alter model outputs. However, existing CFE methods often rely on dataset-specific generative models and incur substantial computational cost, limiting their scalability to high-resolution data. We propose SCE-LITE-HQ, a scalable framework for counterfactual generation that leverages pretrained generative foundation models without task-specific retraining. The method operates in the latent space of the generator, incorporates smoothed gradients to improve optimization stability, and applies mask-based diversification to promote realistic and structurally diverse counterfactuals. We evaluate SCE-LITE-HQ on natural and medical datasets using a desiderata-driven evaluation protocol. Results show that SCE-LITE-HQ produces valid, realistic, and diverse counterfactuals competitive with or outperforming existing baselines, while avoiding the overhead of training dedicated generative models.

Problem

• counterfactual explanations
• high-dimensional visual domains
• generative models
• computational cost
• scalability

Innovation

• counterfactual explanations
• generative foundation models
• smoothed gradients
• latent space optimization
• mask-based diversification