We propose an unsupervised variational model for disentangling video into
independent factors, i.e. each factor's future can be predicted from its past
without considering the others. We show that our approach often learns factors
which are interpretable as objects in a scene.

我们提出了一种无监督的变分模型，用于将视频转换为独立因素，每个因素的未来可以从其过去预测，而无需考虑其他因素。我们展示了我们的方法常常学习到可解释为场景中对象的因素。

使用独立预测来分离视频

Disentangling Video with Independent Prediction

We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We
propose FactorVAE, a method that disentangles by encouraging the distribution
of representations to be factorial and hence independent across the dimensions.
We show that it improves upon $\beta$-VAE by providing a better trade-off
between disentanglement and reconstruction quality. Moreover, we highlight the
problems of a commonly used disentanglement metric and introduce a new metric
that does not suffer from them.

我们提出了一种被称为 FactorVAE 的方法，它可以无监督地学习从独立变化因素生成的数据中发现因素解耦的表示，并通过提供更好的解耦和重构质量之间的平衡得到改进。此外，我们还介绍了一个新的解耦评估度量，并阐明了常用的解耦评估度量存在的问题。

因式分解解开纠缠

Disentangling by Factorising

We study the problem of building models that disentangle independent factors
of variation. Such models could be used to encode features that can efficiently
be used for classification and to transfer attributes between different images
in image synthesis. As data we use a weakly labeled training set. Our weak
labels indicate what single factor has changed between two data samples,
although the relative value of the change is unknown. This labeling is of
particular interest as it may be readily available without annotation costs. To
make use of weak labels we introduce an autoencoder model and train it through
constraints on image pairs and triplets. We formally prove that without
additional knowledge there is no guarantee that two images with the same factor
of variation will be mapped to the same feature. We call this issue the
reference ambiguity. Moreover, we show the role of the feature dimensionality
and adversarial training. We demonstrate experimentally that the proposed model
can successfully transfer attributes on several datasets, but show also cases
when the reference ambiguity occurs.

本文研究了如何使用自编码器模型来解决因素糾纏問題，並通過在圖像對和三元組之間施加約束來訓練它。在實驗中，我們證明了此模型可以成功地在多個數據集上轉移屬性，但也存在參照模糊的情況。