Cross-Inferential Networks for Source-Free Unsupervised Domain Adaptation

Author:

Tang Yushun1,Guo Qinghai2,He Zhihai1

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

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

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

2. Unsupervised Prototype Adapter for Vision-Language Models;Pattern Recognition and Computer Vision;2023-12-24

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