Unsupervised Learning of Disentangled Representation via Auto-Encoding: A Survey

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Deep learning advances enable exploring data's underlying explanatory factors.
• Learning disentangled representations with minimal supervision is a key machine learning challenge.
• Auto-encoding methods and supervised disentanglement metrics are crucial in this field.

Purpose of the Study:

• Provide a theoretical overview of unsupervised representation learning.
• Analyze state-of-the-art methods for unsupervised disentangled representation learning.
• Evaluate and compare existing disentanglement metrics.

Main Methods:

• Review of recent advances in unsupervised representation learning, focusing on auto-encoding.
• Analysis of supervised disentanglement metrics.
• Comparative evaluation of metrics based on modularity, compactness, and informativeness.

Main Results:

• Identified and discussed current state-of-the-art unsupervised disentangled representation learning methods.
• Provided an in-depth analysis of supervised disentanglement quantification metrics.
• Found that the Mutual Information Gap score (MIG) is the only metric satisfying all evaluated criteria.

Conclusions:

• The Mutual Information Gap score (MIG) demonstrates superior performance in evaluating disentangled representations.
• Further research into unsupervised representation learning is essential for improved model generalization.
• Standardized and robust evaluation metrics are critical for advancing the field.