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Separating Bacteria by Capsule Amount Using a Discontinuous Density Gradient
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Inference and Learning for Generative Capsule Models.

Alfredo Nazabal1, Nikolaos Tsagkas2, Christopher K I Williams3,4

  • 1Amazon Development Centre Scotland, Edinburgh EH1 3EG, U.K. alfrena@amazon.com.

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This study introduces a generative model and variational inference algorithms for capsule networks, improving object part assignment and transformation inference. The new methods significantly outperform previous approaches on geometric object and face data.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Capsule networks aim to represent hierarchical relationships between objects and their parts.
  • Existing methods for inferring object-part relationships and transformations can be computationally intensive or limited in scope.

Purpose of the Study:

  • To develop a generative model for capsule network data.
  • To derive variational and RANSAC-based algorithms for inferring object transformations and part assignments.
  • To create a learning algorithm for object models using variational expectation maximization.

Main Methods:

  • Specification of a generative model for capsule network data.
  • Derivation of a variational inference algorithm for object transformation and part assignment.
  • Development of a variational expectation maximization learning algorithm for object models.
  • Application of RANSAC (Random Sample Consensus) for an alternative inference method.

Main Results:

  • Successful inference of object transformations and part assignments on synthetic geometric data (constellations) and face data.
  • Demonstrated significant performance improvement over amortized inference methods (Kosiorek et al., 2019) on constellation data.
  • Validation of the generative model and inference algorithms on complex datasets.

Conclusions:

  • The proposed generative model and variational inference algorithms offer an effective approach for reasoning about object-part relationships in capsule networks.
  • The developed methods provide a more robust and accurate way to infer object transformations and part assignments compared to prior work.
  • This research advances the capabilities of capsule networks in scene understanding and object recognition.