Oracle-Preserving Latent Flows

نویسندگان

چکیده

A fundamental task in data science is the discovery, description, and identification of any symmetries present data. We developed a deep learning methodology for simultaneous discovery multiple non-trivial continuous across an entire labeled dataset. The symmetry transformations corresponding generators are modeled with fully connected neural networks trained specially constructed loss function, ensuring desired properties. two new elements this work use reduced-dimensionality latent space generalization to invariant respect high-dimensional oracles. method demonstrated several examples on MNIST digit dataset, where oracle provided by 10-dimensional vector logits classifier. find classes that transform each image from dataset into synthetic images while conserving values logits. illustrate these as lines equal probability (“flows”) reduced space. These results show can be successfully searched identified interpretable equivalent

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ژورنال

عنوان ژورنال: Symmetry

سال: 2023

ISSN: ['0865-4824', '2226-1877']

DOI: https://doi.org/10.3390/sym15071352