Marginalized Augmented Few-Shot Domain Adaptation.

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Domain Adaptation (DA) enables learning on unlabeled target data using knowledge from a labeled source domain.
• Existing DA methods often struggle with few-shot domain adaptation (FSDA) due to target data scarcity, leading to negative transfer.
• Addressing the cross-domain distribution disparity and insufficient target data is crucial for effective FSDA.

Purpose of the Study:

• Propose a novel Marginalized Augmented FSDA (MAF) approach to tackle the challenges of FSDA.
• Simultaneously address cross-domain distribution disparity and insufficient target data.
• Improve the performance of domain adaptation in low-data scenarios.

Main Methods:

• Introduce Cross-Domain Continuity Augmentation (CCA) to create a continuous domain-invariant latent space.
• Employ Source-Supervised Semantic Augmentation (SSA) to diversify conditional distributions.
• Implement augmentation strategies efficiently using an expected transferable cross-entropy (CE) loss, minimizing computational overhead.

Main Results:

• The proposed MAF approach effectively addresses both distribution disparity and data insufficiency in FSDA.
• Experimental results demonstrate superior performance compared to state-of-the-art methods on various FSDA benchmarks.
• The efficient implementation via expected transferable CE loss incurs negligible extra computing cost.

Conclusions:

• MAF significantly advances the field of few-shot domain adaptation by effectively handling data scarcity and domain shift.
• The novel augmentation strategies provide a robust solution for cross-domain learning with limited target data.
• The method's effectiveness is validated through rigorous experimentation, offering a valuable contribution to machine learning research.