Search Results for author: Shuaicheng Niu

Found 16 papers, 12 papers with code

Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

no code implementations18 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.

Image Classification Semantic Segmentation +1

Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation

1 code implementation27 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.

Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

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.

Image Super-Resolution Test-time Adaptation

Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment

no code implementations22 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.

Pseudo Label Source-Free Domain Adaptation +1

Towards Stable Test-Time Adaptation in Dynamic Wild World

1 code implementation24 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.

Test-time Adaptation

Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation

1 code implementation22 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.

Unsupervised Domain Adaptation

Efficient Test-Time Model Adaptation without Forgetting

1 code implementation6 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.

Test-time Adaptation

Boost Test-Time Performance with Closed-Loop Inference

no code implementations21 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.

Auxiliary Learning

AdaXpert: Adapting Neural Architecture for Growing Data

1 code implementation1 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.

Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation

1 code implementation18 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.

Contrastive Learning Source-Free Domain Adaptation +1

Towards Accurate Text-based Image Captioning with Content Diversity Exploration

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.

Image Captioning

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

1 code implementation30 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.

COVID-19 Diagnosis Domain Adaptation

Disturbance-immune Weight Sharing for Neural Architecture Search

no code implementations29 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.

Neural Architecture Search

Online Adaptive Asymmetric Active Learning with Limited Budgets

1 code implementation18 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.

Active Learning Anomaly Detection

Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis

1 code implementation17 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).

Unsupervised Domain Adaptation

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