Source-Constraint Adversarial Domain Adaptation

@article{Zhong2019SourceConstraintAD,
  title={Source-Constraint Adversarial Domain Adaptation},
  author={Haowen Zhong and Hongya Tuo and Chao Wang and Xuanguang Ren and Jian Hu and Lingfeng Qiao},
  journal={2019 IEEE International Conference on Image Processing (ICIP)},
  year={2019},
  pages={2486-2490}
}
Adversarial adaptation has made great contributions to transfer learning, while the adversarial training strategy lacks of stability when reducing the discrepancy between domains. In this paper, we propose a novel Source-constraint Adversarial Domain Adaptation (SADA) method, which jointly use adversarial adaptation and maximum mean discrepancy (MMD) so that the method can be easily optimized by gradient descent. Furthermore, motivated by metric learning, our method introduces metric loss to… CONTINUE READING

Figures, Tables, and Topics from this paper.

Explore Further: Topics Discussed in This Paper

References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

Coupled Generative Adversarial Networks

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Domain-Adversarial Training of Neural Networks

  • J. Mach. Learn. Res.
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Generative Adversarial Nets

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Adversarial Discriminative Domain Adaptation

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 2 EXCERPTS