AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain

Author:

Kataoka JunORCID,Yoon HyunsooORCID

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference34 articles.

1. Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Wortman, J. (2007). Learning Bounds for Domain Adaptation. In Conference on neural information processing systems. (neurIPS).

2. ImageCLEF 2014: Overview and analysis of the results;Caputo,2014

3. Cui, S., Wang, S., Zhuo, J., Su, C., Huang, Q., & Tian, Q. (2020). Gradually vanishing bridge for adversarial domain adaptation. In IEEE conference on computer vision and pattern recognition. (CVPR).

4. Dizaji, K. G., Herandi, A., Deng, C., Cai, W., & Huang, H. (2017). Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. In International conference on computer vision. (ICCV).

5. Domain-adversarial training of neural networks;Ganin,2017

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