Generating Counterfactual Trajectories with Latent Diffusion Models for
  Concept Discovery
Payal Varshney Adriano Lucieri Christoph Balada Andreas Dengel Sheraz Ahmed
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.