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arXiv · 2305.01777

Representation Learning via Manifold Flattening and Reconstruction

Michael Psenka·Druv Pai·Vishal Raman·Shankar Sastry·Yi Ma
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Abstract

This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our such-generated neural networks, called Flattening Networks (FlatNet), are theoretically interpretable, computationally feasible at scale, and generalize well to test data, a balance not typically found in manifold-based learning methods. We present empirical results and comparisons to other models on synthetic high-dimensional manifold data and 2D image data. Our code is publicly available.

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Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma. 2023-05-02. Representation Learning via Manifold Flattening and Reconstruction. https://arxiv.org/abs/2305.01777

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