Weakly Supervised Disentanglement by Pairwise Similarities.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Generative Models

Background:

• Unsupervised disentanglement learning aims to separate underlying factors of variation in data.
• Deep generative models are popular for disentanglement but lack guaranteed factor recovery without supervision.
• Existing methods struggle to ensure meaningful factors are disentangled without explicit guidance.

Purpose of the Study:

• To introduce a novel weakly supervised approach for disentanglement learning.
• To address the limitations of purely unsupervised methods in factor recovery.
• To enable users to guide the disentanglement process with minimal input.

Main Methods:

• Proposing a new method for weakly supervised disentanglement within the Variational Autoencoder framework.
• Utilizing user-provided similarity information between data instances.
• Employing binary (yes/no) or real-valued labels to indicate instance similarity based on a target factor.

Main Results:

• Weak supervision significantly improves the performance of disentanglement methods.
• The proposed approach successfully recovers factors of interest more reliably than unsupervised methods.
• Experimental results validate the effectiveness of incorporating weak supervision.

Conclusions:

• Weakly supervised disentanglement offers a practical solution to improve factor recovery in deep generative models.
• User-provided similarity information is a valuable form of weak supervision for disentanglement.
• This approach enhances the interpretability and utility of deep generative models.