Mutual Variational Inference: An Indirect Variational Inference Approach for Unsupervised Domain Adaptation

Area of Science:

• Machine Learning
• Computer Vision

Background:

• Unsupervised domain adaptation aims to generalize models trained on labeled data to unlabeled target domains.
• Effective knowledge transfer from labeled to unlabeled datasets is crucial for model performance.

Purpose of the Study:

• To address the unsupervised domain adaptation problem by leveraging latent variables for knowledge transfer.
• To propose a novel variational inference approach for approximating latent distributions.

Main Methods:

• Utilizing latent variables from the encoder as agents for knowledge transfer.
• Employing a variational inference approach to approximate latent distributions in unlabeled data.
• Implementing a regularization technique to progressively transfer discriminative knowledge.

Main Results:

• The proposed method demonstrates superior performance in unsupervised domain adaptation.
• Consistent outperformance of state-of-the-art methods on benchmark datasets for object and digit classification.
• Validation of the importance of latent variables for effective knowledge transfer.

Conclusions:

• The novel variational inference approach effectively transfers discriminative knowledge using latent representations.
• The method offers a significant advancement in unsupervised domain adaptation for classification tasks.
• Latent variable estimation is key to improving model generalization in cross-domain learning.