CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction

Area of Science:

• Computational learning theory
• Machine learning
• Information theory

Background:

• Existing generative models like GANs and VAEs have limitations in handling multi-class, multi-dimensional real-world data.
• Learning structured representations that are both discriminative and generative remains a challenge.

Purpose of the Study:

• To propose a new computational framework for learning structured generative models for real-world datasets.
• To develop a method that unifies auto-encoding and GANs for enhanced representation learning.
• To enable the learning of both discriminative and generative representations for multi-class, multi-dimensional data.

Main Methods:

• Formulating representation learning as a two-player minimax game between encoder and decoder.
• Utilizing a rate reduction utility function, an information-theoretic measure for distances between subspace-like Gaussians.
• Drawing inspiration from closed-loop error feedback in control systems to avoid complex distribution comparisons.

Main Results:

• The proposed closed-loop framework demonstrates competitive or superior visual quality and classification performance compared to existing GAN and VAE-based methods.
• Learned features are structured, with distinct classes mapped to independent principal subspaces.
• Intra-class variations are modeled by independent principal components within each subspace.

Conclusions:

• The new closed-loop formulation offers a unified approach to representation learning, extending auto-encoding and GAN concepts.
• The method effectively learns structured, discriminative, and generative representations for complex datasets.
• Experimental results on benchmark datasets validate the framework's potential and effectiveness.