VTAE: Variational Transformer Autoencoder With Manifolds Learning.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Machine Learning

Background:

• Deep generative models learn data distributions using latent variables and non-linear generators.
• Non-linear generators often lead to poor latent space projections and weak representation learning.
• Riemannian metrics can address these projection issues by improving data representation on manifolds.

Purpose of the Study:

• To propose a Variational spatial-Transformer AutoEncoder (VTAE) for enhanced representation learning in deep generative models.
• To improve the accuracy of interpolations between data samples by minimizing geodesics on a Riemannian manifold.
• To achieve smoother and more plausible interpolations compared to existing linear methods.

Main Methods:

• Developed a Variational spatial-Transformer AutoEncoder (VTAE) integrating spatial-transformers within a variational autoencoder framework.
• Employed geodesic computation on a Riemannian manifold to model latent variables and data distributions.
• Introduced a novel geodesic interpolation network for latent space traversal.

Main Results:

• The VTAE model demonstrated improved representation learning by explicitly mapping data onto a Riemannian manifold.
• Geodesic interpolation resulted in smoother and more plausible transitions between latent representations.
• Experiments showed enhanced predictive accuracy and versatility in computer vision tasks like image interpolation and reconstruction.

Conclusions:

• Minimizing geodesics on Riemannian manifolds significantly improves deep generative model performance.
• The proposed VTAE with geodesic interpolation offers a superior approach for representation learning and data manipulation.
• This method advances the capabilities of generative models in various computer vision applications.