We construct an unsupervised learning model that achieves nonlinear
disentanglement of underlying factors of variation in naturalistic videos.
Previous work suggests that representations can be disentangled if all but a
few factors in the environment stay constant at any point in time. As a result,
algorithms proposed for this problem have only been tested on carefully
constructed datasets with this exact property, leaving it unclear whether they
will transfer to natural scenes. Here we provide evidence that objects in
segmented natural movies undergo transitions that are typically small in
magnitude with occasional large jumps, which is characteristic of a temporally
sparse distribution. We leverage this finding and present SlowVAE, a model for
unsupervised representation learning that uses a sparse prior on temporally
adjacent observations to disentangle generative factors without any assumptions
on the number of changing factors. We provide a proof of identifiability and
show that the model reliably learns disentangled representations on several
established benchmark datasets, often surpassing the current state-of-the-art.
We additionally demonstrate transferability towards video datasets with natural
dynamics, Natural Sprites and KITTI Masks, which we contribute as benchmarks
for guiding disentanglement research towards more natural data domains.

该论文介绍了一种基于 SlowVAE 的非监督学习模型， 可以在自然视频中实现底层变化因素的非线性分离， 并证明该模型在多个测试数据集上都超过了目前的最先进水平，并成功应用于其他具有自然动态的视频数据集。