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Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, Mingkui Tan. Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16888-16905, 2022. Abstract.
Shuaicheng Niu* 1 2 Jiaxiang Wu* 3 Yifan Zhang* 4 Yaofo Chen1 Shijian Zheng1 Peilin Zhao3 Mingkui Tan1 5 Abstract Test-time adaptation (TTA) seeks to tackle po-tential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly impor-tant for deep models when the test ...
24 de feb. de 2023 · Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world. Specifically, TTA may fail to improve or even harm the model performance ...
This is the official project repository for Towards Stable Test-Time Adaptation in Dynamic Wild World 🔗 by Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao and Mingkui Tan (ICLR 2023 Oral, Notable-Top-5%).. 1️⃣ SAR conducts model learning at test time to adapt a pre-trained model to test data that has distributional shifts ☀️ 🌧 ️, such as ...
Quantized Convolutional Neural Networks for Mobile Devices. Recently, convolutional neural networks (CNN) have demonstrated impressi... 0 Jiaxiang Wu, et al. ∙. share. Read Jiaxiang Wu's latest research, browse their coauthor's research, and play around with their algorithms.
1 de dic. de 2021 · We propose a novel disturbance-immune WS training scheme for NAS. By updating models in an orthogonal space with orthogonal gradient decent, our method exhibits more stable/accurate performance estimation for NAS. Equipped with the disturbance-immune WS, NAS is able to learn a better search strategy and find better architectures.
Yuxuan Wang*, Haixu Wu*, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long# arXiv 2024. Journal Articles. Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model Haixu Wu, Hang Zhou, Mingsheng Long#, Jianmin Wang# Nature Machine Intelligence 2023 / PDF / Code / Slides (Cover Paper)
