Model identifiability is a desirable property in the context of unsupervised
representation learning. In absence thereof, different models may be
observationally indistinguishable while yielding representations that are
nontrivially related to one another, thus making the recovery of a ground truth
generative model fundamentally impossible, as often shown through suitably
constructed counterexamples. In this note, we discuss one such construction,
illustrating a potential failure case of an identifiability result presented in
"Desiderata for Representation Learning: A Causal Perspective" by Wang & Jordan
(2021). The construction is based on the theory of nonlinear independent
component analysis. We comment on implications of this and other
counterexamples for identifiable representation learning.

本文讨论了一种可能的未标识结果，说明了非线性独立成分分析理论基础上的建构，并通过适当构造的反例说明了表示学习中的其他反例及其可识别性。