用预训练生成模型生成高分辨率视觉反事实解释，无需额外训练
SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models
- 在生成器潜空间中优化，用平滑梯度提升稳定性
- 生成的反事实图像真实且多样，在多个数据集上表现优于基线
- 适用于医疗和自然图像，适合追求可解释性的研究者
现代神经网络在高维视觉领域表现优异，但可解释性差。反事实解释（CFE）通过寻找使模型输出改变的最小输入变化来解释黑箱预测。现有方法常依赖特定数据集的生成模型，计算开销大，难以扩展到高分辨率数据。我们提出SCE-LITE-HQ，一种基于预训练生成基础模型、无需任务特定重训练的可扩展反事实生成框架。该方法在生成器潜空间中操作，引入平滑梯度以提高优化稳定性，并采用基于掩码的多样化策略，生成更真实、结构多样的反事实图像。我们在自然和医学数据集上使用目标驱动评估协议进行测试。结果表明，SCE-LITE-HQ生成的反事实有效、真实且多样，性能媲美或超越现有基线，同时避免了训练专用生成模型的开销。
原文摘要 · Abstract (English)
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.
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