  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.
