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Holographic and other Point Set Distances for Machine Learning

2019-05-01 · ICLR 2019 5 · Lukas Balles, Thomas Fischbacher

We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications. We compare our novel construction to other point set distance functions and show proof of concept experiments for training neural networks end-to-end on point set prediction tasks such as object detection.

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BIG-bench Machine Learningobject-detectionObject Detection

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