DCI disentanglement framework (Disentanglement / Completeness / Informativeness)

Quantifies how cleanly a set of learned latent codes maps onto a set of known ground-truth generative factors along three separate axes: Disentanglement (does each code capture at most one factor), Completeness (is each factor captured by as few codes as possible), and Informativeness (can a factor be predicted from the codes at all) — typically operationalized via a Lasso/regression importance matrix between codes and factors.