Unsupervised disentanglement of content and transformation has recently drawn
much research, given their efficacy in solving downstream unsupervised tasks
like clustering, alignment, and shape analysis. This problem is particularly
important for analyzing shape-focused real-world scientific image datasets,
given their significant relevance to downstream tasks. The existing works
address the problem by explicitly parameterizing the transformation factors,
significantly reducing their expressiveness. Moreover, they are not applicable
in cases where transformations can not be readily parametrized. An alternative
to such explicit approaches is self-supervised methods with data augmentation,
which implicitly disentangles transformations and content. We demonstrate that
the existing self-supervised methods with data augmentation result in the poor
disentanglement of content and transformations in real-world scenarios.
Therefore, we developed a novel self-supervised method, DualContrast,
specifically for unsupervised disentanglement of content and transformations in
shape-focused image datasets. Our extensive experiments showcase the
superiority of DualContrast over existing self-supervised and explicit
parameterization approaches. We leveraged DualContrast to disentangle protein
identities and protein conformations in cellular 3D protein images. Moreover,
we also disentangled transformations in MNIST, viewpoint in the Linemod Object
dataset, and human movement deformation in the Starmen dataset as
transformations using DualContrast.

我们开发了一种名为 DualContrast 的新型自监督方法，专门用于解决与形状相关的图像数据集中的内容和变换的无监督分离问题。我们的广泛实验证明了 DualContrast 相对于现有的自监督和显式参数化方法的优越性。我们利用 DualContrast 在细胞 3D 蛋白质图像中分离了蛋白质的身份和构象，以及在 MNIST、Linemod Object 数据集中的视角以及在 Starmen 数据集中的人体运动变形作为变换。