GMFAD: Towards Generalized Visual Recognition via Multilayer Feature Alignment and Disentanglement

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning for visual recognition assumes similar data distributions, which often fails in real-world scenarios.
• Existing cross-domain methods often overlook inter-dimension and inter-sample correlations, limiting transferability.
• Hierarchical feature learning in deep networks offers potential for improved generalization.

Purpose of the Study:

• To propose a novel feature learning framework, GMFAD, to enhance generalization capability in visual recognition tasks.
• To address the challenge of domain shift by learning transferable feature representations.
• To improve the performance of deep learning models in cross-domain visual recognition.

Main Methods:

• Developed the GMFAD (Generalizable Multi-feature Alignment and Disentanglement) framework.
• Learned shallow layer features by aligning domain divergence using inter-dimension and inter-sample correlations.
• Performed deep feature disentanglement at higher layers to enhance feature transferability.
• Utilized a multilayer perceptron approach for feature learning.

Main Results:

• GMFAD demonstrated superior performance in learning transferable feature representations compared to state-of-the-art methods.
• Experiments across various visual recognition tasks validated the framework's effectiveness.
• The proposed method successfully addressed domain divergence issues.

Conclusions:

• The GMFAD framework effectively learns generalizable feature representations for visual recognition.
• Aligning domain divergence and disentangling deep features are crucial for cross-domain transferability.
• This approach offers a promising solution for practical visual recognition applications with domain shift.