Supplementary - Designing Counterfactual Generators using Deep Model Inversion

We adopted the existing code from Amersfoort et al. to train the DUQ models. DIP/INR and the proposed manifold consistency, it can still be challenging to avoid trivial solutions. However, given the large solution space, this often leads to unrealistic images. For this experiment, we used the CelebA faces dataset and considered the baldness attribute. Figure 1: Examples of counterfactuals generated for the baldness and age attributes using DISC.