Delving Into Generalizable Label Distribution Learning.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Label Distribution Learning (LDL) excels at handling label ambiguity but is limited by the i.i.d. assumption.
• Existing LDL methods degrade in performance on out-of-distribution data, restricting practical applications.
• The core issue is label annotations changing across different data domains.

Purpose of the Study:

• To address the limitations of standard LDL in out-of-distribution scenarios.
• To introduce and investigate the problem of Generalizable Label Distribution Learning (GLDL).
• To develop novel methods for robust LDL across diverse data domains.

Main Methods:

• Investigated the characteristics of GLDL, identifying domain-specific label annotation shifts.
• Explored domain-invariant feature-label correlation information.
• Proposed two practical methods to mitigate performance degradation in GLDL.

Main Results:

• The proposed methods demonstrate superior performance in extensive experiments.
• The study validates the effectiveness of utilizing domain-invariant feature-label correlations.
• The research provides a significant advancement for practical GLDL problems.

Conclusions:

• The developed GLDL methods effectively handle label ambiguity across domains.
• This work establishes new benchmarks and techniques for generalizable label distribution learning.
• The findings significantly expand the applicability of LDL in real-world, diverse datasets.