Learning to Transfer Examples for Partial Domain Adaptation

Abstract

Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across the source and target domains, and a progressive weighting scheme that quantifies the transferability of source examples while controlling their importance to the learning task in the target domain. A thorough evaluation on several benchmark datasets shows that our approach achieves state-of-the-art results for partial domain adaptation tasks.

CVPR 2019 accepted

References in corpus (10)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Natural Language Processing (almost) from Scratch
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• How transferable are features in deep neural networks?
• Learning Transferable Features with Deep Adaptation Networks
• Deep Domain Confusion: Maximizing for Domain Invariance
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Unsupervised Domain Adaptation with Residual Transfer Networks
• Domain Adaptation: Learning Bounds and Algorithms
• LSDA: Large Scale Detection Through Adaptation

Cited by in corpus (6)

• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
• Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation
• Prototypical Partial Optimal Transport for Universal Domain Adaptation
• Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation
• Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation
• Self-Adaptive Partial Domain Adaptation