Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space

Understand

To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) and learn a probabilistic model in the latent space.

• However, they struggle to accurately reproduce the probability distribution function (PDF) in the input space from that in the latent space.
• If the embedding were isometric, this issue can be solved, because the relation of PDFs can become tractable.
• To achieve isometric property, we propose Rate- Distortion Optimization guided autoencoder inspired by orthonormal transform coding.