  Conditional Variational Auto Encoders (VAE) are gathering significant
attention as an Explainable Artificial Intelligence (XAI) tool. The codes in
the latent space provide a theoretically sound way to produce counterfactuals,
i.e. alterations resulting from an intervention on a targeted semantic feature.
To be applied on real images more complex models are needed, such as
Hierarchical CVAE. This comes with a challenge as the naive conditioning is no
longer effective. In this paper we show how relaxing the effect of the
posterior leads to successful counterfactuals and we introduce VAEX an
Hierarchical VAE designed for this approach that can visually audit a
classifier in applications.
