Disentangled representations, where the higher level data generative factors
are reflected in disjoint latent dimensions, offer several benefits such as
ease of deriving invariant representations, transferability to other tasks,
interpretability, etc. We consider the problem of unsupervised learning of
disentangled representations from large pool of unlabeled observations, and
propose a variational inference based approach to infer disentangled latent
factors. We introduce a regularizer on the expectation of the approximate
posterior over observed data that encourages the disentanglement. We also
propose a new disentanglement metric which is better aligned with the
qualitative disentanglement observed in the decoder's output. We empirically
observe significant improvement over existing methods in terms of both
disentanglement and data likelihood (reconstruction quality).

本文提出一个基于变分推理的方法，从大量未标记的观察中推断分离的潜在因素，通过对所观察数据的近似后验期望引入正则化项，从而鼓励分离；同时，提出了一种新的分离度量，与解码器输出中观察到的定性分离更加一致，经实验证明，在分离度和数据似然（重建质量）方面存在显著的改进。

从未标记的观测中变分推断出解耦的潜在概念

Variational Inference of Disentangled Latent Concepts from Unlabeled  Observations

We develop two iterative algorithms for solving the low rank phase retrieval
(LRPR) problem. LRPR refers to recovering a low-rank matrix $\X$ from
magnitude-only (phaseless) measurements of random linear projections of its
columns. Both methods consist of a spectral initialization step followed by an
iterative algorithm to maximize the observed data likelihood. We obtain sample
complexity bounds for our proposed initialization approach to provide a good
approximation of the true $\X$. When the rank is low enough, these bounds are
significantly lower than what existing single vector phase retrieval algorithms
need. Via extensive experiments, we show that the same is also true for the
proposed complete algorithms.

本文提出了两种迭代算法来解决低秩相位恢复问题，这些算法由谱初始化步骤和迭代算法组成，旨在最大化观测数据可能性，并得到了相应的样本复杂度界限。经过大量实验，表明这些算法在低秩相位恢复问题上具有良好的效果。