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Moment Matching for Multi-Source Domain AdaptationXingchao Peng1, Qinxun Bai1, Xide Xia1, Zijun Huang2, Kate Saenko1, Bo Wang3

1Boston University 2Columbia University 3Vector Institute

Introduction

We propose a novel approach called

M3SDA to tackle multi-source domain

adaptation.

We derive a sound error bounds for

domain adaptation approach.

We collect and annotate a large scale

dataset called DomainNet, which

contains six distinct domains, 345

categories and ~0.6 million images.

Overview

M3SDA Model Analysis

Conclusion

We have propose a novel method, M3SDA,

to align multiple source domains together

with the target domain.

We have also collected, annotated and

evaluated by far the largest domain

adaptation dataset named DomainNet.

Experimental Results

DomainNet Dataset

mixed

Dataset: http://ai.bu.edu/DomainNet/

Challenge: http://ai.bu.edu/visda-2019/

Acknowledge:This work was partially supported by NSF and Honda

Research Institute. The authors also acknowledge

support from CIFAR AI Chairs Program.