Chart Auto-Encoders for Manifold Structured Data
This work addresses the limitation of flat latent spaces in auto-encoders for manifold-structured data, offering a novel approach with potential broad impact in representation learning, though it is incremental in advancing geometric methods.
The authors tackled the problem of representing manifold-structured data in deep generative models by proposing a Chart Auto-Encoder (CAE) with a multi-chart latent space, which provides high-fidelity reconstruction, preserves proximity, and generates data near the manifold, outperforming existing auto-encoders.
Deep generative models have made tremendous advances in image and signal representation learning and generation. These models employ the full Euclidean space or a bounded subset as the latent space, whose flat geometry, however, is often too simplistic to meaningfully reflect the manifold structure of the data. In this work, we advocate the use of a multi-chart latent space for better data representation. Inspired by differential geometry, we propose a \textbf{Chart Auto-Encoder (CAE)} and prove a universal approximation theorem on its representation capability. We show that the training data size and the network size scale exponentially in approximation error with an exponent depending on the intrinsic dimension of the data manifold. CAE admits desirable manifold properties that auto-encoders with a flat latent space fail to obey, predominantly proximity of data. We conduct extensive experimentation with synthetic and real-life examples to demonstrate that CAE provides reconstruction with high fidelity, preserves proximity in the latent space, and generates new data remaining near the manifold. These experiments show that CAE is advantageous over existing auto-encoders and variants by preserving the topology of the data manifold as well as its geometry.