Ordinal Unsupervised Domain Adaptation With Recursively Conditional Gaussian Imposed Variational Disentanglement.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Unsupervised domain adaptation (UDA) is crucial for scalability but often overlooks tasks with ordered labels.
• Existing UDA methods typically handle independently discrete labels, not successive ones common in medical diagnosis.
• Ordinal classification UDA requires imposing an ordinal distribution prior on the latent space.

Purpose of the Study:

• To develop a UDA method for ordinal classification tasks with successively distributed labels.
• To introduce a novel approach for modeling ordinal distributions within the latent space for UDA.
• To enhance domain alignment in cross-domain ordinal classification.

Main Methods:

• Defined a partially ordered set (poset) to constrain latent vectors for ordinal classification.
• Proposed a recursively conditional Gaussian (RCG) set for ordered constraint modeling, offering a tractable joint distribution prior.
• Disentangled cross-domain images into shared ordinal content and separate unrelated spaces, applying self-training exclusively to the shared space for ordinal-aware domain alignment.

Main Results:

• The proposed RCG set effectively models ordered constraints in UDA.
• Controlled violation of poset constraints using a "three-sigma rule."
• Demonstrated significant effectiveness in UDA for medical diagnoses and facial age estimation.

Conclusions:

• The novel UDA approach effectively addresses ordinal classification challenges.
• The RCG prior and poset constraint enable robust ordinal-aware domain alignment.
• The method shows promise for real-world applications like medical image analysis and age estimation.