Cross-Inferential Networks for Source-Free Unsupervised Domain Adaptation
Author:
Affiliation:
1. Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
2. Huawei Technologies Co., Ltd.,Advanced Computing and Storage Laboratory,Shenzhen,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10221937/10221892/10222707.pdf?arnumber=10222707
Reference24 articles.
1. Neuromodulated hebbian learning for fully test-time adaptation;tang;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2023
2. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
3. Tent: Fully test-time adaptation by entropy minimization;wang;International Conference on Learning Representations,2021
4. Visda: The visual domain adaptation challenge;peng,2017
5. Learning discrete representations via information maximizing self-augmented training;hu;International Conference on Machine Learning,2017
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