Secure Domain Adaptation with Multiple Sources

Abstract

Multi-source unsupervised domain adaptation (MUDA) is a framework to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple annotated source domains. When the source domains are distributed, data privacy and security can become significant concerns and protocols may limit data sharing, yet existing MUDA methods overlook these constraints. We develop an algorithm to address MUDA when source domain data cannot be shared with the target or across the source domains. Our method is based on aligning the distributions of source and target domains indirectly via estimating the source feature embeddings and predicting over a confidence based combination of domain specific model predictions. We provide theoretical analysis to support our approach and conduct empirical experiments to demonstrate that our algorithm is effective.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Learning Transferable Features with Deep Adaptation Networks
• Semantic Segmentation using Adversarial Networks
• Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
• Domain Aggregation Networks for Multi-Source Domain Adaptation
• Your Classifier can Secretly Suffice Multi-Source Domain Adaptation