DVIB is an information bottleneck method that tries to disentangle multiview data into shared and private representations. The implementation is based on the implementation from Multi-view information bottleneck (MIB, Marco Federici, et al, 2020). DVIB used the same wrap function as MIB demonstration. Please refer to https://github.com/mfederici/Multi-View-Information-Bottleneck to run the code.
Multiview data combine information from multiple modalities and have potentials to provide more comprehensive features for diverse machine learning tasks. A fundamental question in multiview learning is how additional views provide effective information compared with single view and is it possible to quantitatively identify this addtional information.
In this work, we try to tackle this challenge by decomposing the entangled multiview features into shared latent representations that are common across all views and private representations that are specific to each single view. We formulate this feature disentabglement in the framework of information bottleneck and propose disentangled variational information bottleneck (DVIB). DVIB explicitly defines the properties of shared and private representations using constrains from mutual information. By deriving variational upper and lower bounds of mutual information terms, representations are efficiently optimized.
Fig. 1 | An overview of the study. (a) Single-cell spatial transcriptomics technologies provide cell infromation from both transcriptional and morphological modalities, which reveal cell heterogeneity from different angles. (b) Cell differences in the tissue can be characterized based on each single modalties. (c) By combining both heterogeneities from two modalities, fine-grained cell subpopulations can be revealed.
DVIB implemented based on multi-view information bottleneck https://github.com/mfederici/Multi-View-Information-Bottleneck. We appreciate the authors in releasing the code.
DVIB was tested with the following packages:
- Python >= 3.6
- Pytorch >= 1.18.0
- Numpy >= 1.16.2
- Scipy >= 1.1.1
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