Mutual Learning Network for Multi-Source Domain Adaptation

by   Zhenpeng Li, et al.

Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. However, in practice the labeled data can come from multiple source domains with different distributions. In such scenarios, the single source domain adaptation methods can fail due to the existence of domain shifts across different source domains and multi-source domain adaptation methods need to be designed. In this paper, we propose a novel multi-source domain adaptation method, Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA). Under the framework of mutual learning, the proposed method pairs the target domain with each single source domain to train a conditional adversarial domain adaptation network as a branch network, while taking the pair of the combined multi-source domain and target domain to train a conditional adversarial adaptive network as the guidance network. The multiple branch networks are aligned with the guidance network to achieve mutual learning by enforcing JS-divergence regularization over their prediction probability distributions on the corresponding target data. We conduct extensive experiments on multiple multi-source domain adaptation benchmark datasets. The results show the proposed ML-MSDA method outperforms the comparison methods and achieves the state-of-the-art performance.


Ensemble Multi-Source Domain Adaptation with Pseudolabels

Given multiple source datasets with labels, how can we train a target mo...

Multi-source Domain Adaptation for Visual Sentiment Classification

Existing domain adaptation methods on visual sentiment classification ty...

Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems

Domain Adaptation arises when we aim at learning from source domain a mo...

StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization

Domain adaptation for semantic segmentation has recently been actively s...

Multi-step domain adaptation by adversarial attack to ℋ Δℋ-divergence

Adversarial examples are transferable between different models. In our p...

Tackling unsupervised multi-source domain adaptation with optimism and consistency

It has been known for a while that the problem of multi-source domain ad...

Dual Adversarial Domain Adaptation

Unsupervised domain adaptation aims at transferring knowledge from the l...

Please sign up or login with your details

Forgot password? Click here to reset