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In representation learning, there has been recent interest in developingalgorithms to disentangle the ground-truth generative factors behind a dataset,and metrics to quantify how fully this occurs. However, these algorithms andmetrics often assume that both representations and ground-truth factors areflat, continuous, and factorized, whereas many real-world generative processesinvolve rich hierarchical structure, mixtures of discrete and continuousvariables with dependence between them, and even varying intrinsicdimensionality. In this work, we develop benchmarks, algorithms, and metricsfor learning such hierarchical representations.

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement