Human expertise depends on the ability to recognize subtle visual differences, such as distinguishing diseases, species, or celestial phenomena. We propose a new method to teach novices how to differentiate between nuanced categories in specialized domains. Our method uses generative models to visualize the minimal change in features to transition between classes, i.e., counterfactuals, and performs well even in domains where data is sparse, examples are unpaired, and category boundaries are not easily explained by text. By manipulating the conditioning space of diffusion models, our proposed method DIFFusion disentangles category structure from instance identity, enabling high-fidelity synthesis even in challenging domains. Experiments across six domains show accurate transitions even with limited and unpaired examples across categories. User studies confirm that our generated counterfactuals outperform unpaired examples in teaching perceptual expertise, showing the potential of generative models for specialized visual learning.

Our approach leverages diffusion models to create smooth transitions between visual categories, helping novices learn subtle discriminative features. By carefully manipulating the conditioning space, we maintain instance identity while traversing category boundaries.

DIFFusion generates smooth transitions that highlight key discriminative features between categories.

Our user studies demonstrate that DIFFusion significantly improves novices' ability to distinguish subtle visual differences.

We thank our user study participants and collaborators for their insights. This work is supported by the Carver Mead New Adventures Fund, a Pritzker Award, an AI4Science Amazon Discovery Grant, the NSF AI Institute for Artificial and Natural Intelligence (ARNI), NSF CAREER #2046910, NSF RETTL #2202578, DARPA ECOLE, and a Google Fellowship. Views are ours, not necessarily our sponsors'.