no code implementations • 2 Apr 2024 • Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu, Peilin Zhao
However, in real-world scenarios, models are usually deployed on resource-limited devices, e. g., FPGAs, and are often quantized and hard-coded with non-modifiable parameters for acceleration.
no code implementations • 18 Mar 2024 • Mingkui Tan, Guohao Chen, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Peilin Zhao, Shuaicheng Niu
To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA.
1 code implementation • 27 Feb 2024 • Yaofo Chen, Shuaicheng Niu, Shoukai Xu, Hengjie Song, YaoWei Wang, Mingkui Tan
Moreover, with the increasing data collected at the edge, this paradigm also fails to further adapt the cloud model for better performance.
1 code implementation • NeurIPS 2023 • Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu, Thomas H. Li, Bohan Zhuang, Mingkui Tan
Then, we adapt the SR model by implementing feature-level reconstruction learning from the initial test image to its second-order degraded counterparts, which helps the SR model generate plausible HR images.
no code implementations • 22 May 2023 • Hongbin Lin, Mingkui Tan, Yifan Zhang, Zhen Qiu, Shuaicheng Niu, Dong Liu, Qing Du, Yanxia Liu
To address this issue, we study a more practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class distributions of both the unseen source domain and unlabeled target domain are unknown and could be arbitrarily skewed.
1 code implementation • 24 Feb 2023 • Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Mingkui Tan
In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability.
1 code implementation • 22 Jul 2022 • Hongbin Lin, Yifan Zhang, Zhen Qiu, Shuaicheng Niu, Chuang Gan, Yanxia Liu, Mingkui Tan
2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge.
1 code implementation • 6 Apr 2022 • Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w. r. t.
no code implementations • 21 Mar 2022 • Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Guanghui Xu, Haokun Li, Peilin Zhao, Junzhou Huang, YaoWei Wang, Mingkui Tan
Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.
1 code implementation • 1 Jul 2021 • Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu, Yifan Zhang, Yong Guo, Peilin Zhao, Peng Wang, Mingkui Tan
To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data.
1 code implementation • 18 Jun 2021 • Zhen Qiu, Yifan Zhang, Hongbin Lin, Shuaicheng Niu, Yanxia Liu, Qing Du, Mingkui Tan
(2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes.
Ranked #12 on Domain Adaptation on VisDA2017
1 code implementation • CVPR 2021 • Guanghui Xu, Shuaicheng Niu, Mingkui Tan, Yucheng Luo, Qing Du, Qi Wu
This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively.
1 code implementation • 5 Jul 2020 • Yifan Zhang, Ying WEI, Qingyao Wu, Peilin Zhao, Shuaicheng Niu, Junzhou Huang, Mingkui Tan
Deep learning based medical image diagnosis has shown great potential in clinical medicine.
1 code implementation • 30 Apr 2020 • Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying WEI, Peilin Zhao, Jianhua Yao, Junzhou Huang, Qingyao Wu, Mingkui Tan
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
no code implementations • 29 Mar 2020 • Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yong Guo, Peilin Zhao, Junzhou Huang, Mingkui Tan
To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.
1 code implementation • 18 Nov 2019 • Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan
In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.
1 code implementation • 17 Nov 2019 • Yifan Zhang, Ying WEI, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, Mingkui Tan, Junzhou Huang
In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).