Abstract
Trustworthiness is a major prerequisite for the safe application of opaque deep learning models in high-stakes domains like medicine. Understanding the decision-making process not only contributes to fostering trust but might also reveal previously unknown decision criteria of complex models that could advance the state of medical research. The discovery of decision-relevant concepts from black box models is a particularly challenging task. This study proposes Concept Discovery through Latent Diffusion-based Counterfactual Trajectories (CDCT), a novel three-step framework for concept discovery leveraging the superior image synthesis capabilities of diffusion models. In the first step, CDCT uses a Latent Diffusion Model (LDM) to generate a counterfactual trajectory dataset. This dataset is used to derive a disentangled representation of classification-relevant concepts using a Variational Autoencoder (VAE). Finally, a search algorithm is applied to identify relevant concepts in the disentangled latent space. The application of CDCT to a classifier trained on the largest public skin lesion dataset revealed not only the presence of several biases but also meaningful biomarkers. Moreover, the counterfactuals generated within CDCT show better FID scores than those produced by a previously established state-of-the-art method, while being 12 times more resource-efficient. Unsupervised concept discovery holds great potential for the application of trustworthy AI and the further development of human knowledge in various domains. CDCT represents a further step in this direction.
Abstract (translated)
信任度是深度学习中奥秘深度学习模型在高风险领域（如医疗领域）安全应用的重要前提条件。理解决策过程不仅有助于增强信任，还可能揭示复杂模型的 previously unknown 决策标准，有助于推动医学研究的发展。从黑盒模型中发现相关概念是一个具有挑战性的任务。本研究提出了通过基于潜在扩散的逆向传播（CDCT）进行概念发现的新颖的三步框架，利用扩散模型的卓越图像合成能力。在第一步中，CDCT 使用 Latent Diffusion Model (LDM) 生成反事实轨迹数据集。这个数据集用于通过变分自编码器（VAE）获得分类相关概念的分离表示。最后，应用搜索算法在分离的潜在空间中识别相关概念。将 CDCT 应用于在公共皮肤斑疹数据集上训练的分类器揭示了不仅存在几种偏见，而且还有有意义的生物标记物。此外，CDCT 生成的反事实例在 FID 分数上优于之前确定的最先进方法，而资源消耗量降低了 12 倍。无监督的概念发现对于信任 AI 的应用和各个领域的知识进一步发展具有巨大的潜力。CDCT 代表了这一方向的一个进一步步骤。
URL
https://arxiv.org/abs/2404.10356
