# Learning Identity-Preserving Transformations on Data Manifolds

@article{Connor2021LearningIT, title={Learning Identity-Preserving Transformations on Data Manifolds}, author={Marissa Connor and Kion Fallah and Christopher J. Rozell}, journal={ArXiv}, year={2021}, volume={abs/2106.12096} }

Many machine learning techniques incorporate identity-preserving transformations into their models to generalize their performance to previously unseen data. These transformations are typically selected from a set of functions that are known to maintain the identity of an input when applied (e.g., rotation, translation, flipping, and scaling). However, there are many natural variations that cannot be labeled for supervision or defined through examination of the data. As suggested by the…

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