Learning Hyperbolic Representations for Unsupervised 3D Segmentation

Date: June 12, 2020 1:00pm (remote)

Speaker: Joy Hsu (Stanford)

There exists a need for unsupervised 3D segmentation on complex volumetric data in the case of limited annotations or for tasks of object discovery -- especially in biomedical fields. We efficient learn representations for unsupervised segmentation through hyperbolic embeddings that model hierarchy innate to 3D input. Our method learns hyperbolic representation through a novel gyroplane convolutional layer as well as a hierarchical triplet loss, and retrieves multi-level segmentations from clustering on hyperbolic space. We show the effectiveness of our method on three biologically-inspired datasets, including one on cryogenic electron microscopy (cryo-EM), with images supplied by SLAC.

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