1 code implementation • LREC 2022 • Jiashu Pu, Ziyi Huang, Yadong Xi, Guandan Chen, WeiJie Chen, Rongsheng Zhang
As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications.
2 code implementations • CVPR 2022 • Binbin Chen, WeiJie Chen, Shicai Yang, Yunyi Xuan, Jie Song, Di Xie, ShiLiang Pu, Mingli Song, Yueting Zhuang
To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.
2 code implementations • CVPR 2022 • Rang Meng, WeiJie Chen, Shicai Yang, Jie Song, Luojun Lin, Di Xie, ShiLiang Pu, Xinchao Wang, Mingli Song, Yueting Zhuang
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
no code implementations • 13 Jun 2022 • Yilu Guo, Shicai Yang, WeiJie Chen, Liang Ma, Di Xie, ShiLiang Pu
Therefore, it is crucial to study how to learn more discriminative representations while avoiding over-fitting.
no code implementations • 13 Jun 2022 • Junchu Huang, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang
This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques.
1 code implementation • 13 Jun 2022 • Meilin Chen, WeiJie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, ShiLiang Pu
In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter.
1 code implementation • 27 May 2022 • Zhishu Sun, Zhifeng Shen, Luojun Lin, Yuanlong Yu, Zhifeng Yang, Shicai Yang, WeiJie Chen
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
1 code implementation • ACL 2022 • WeiJie Chen, Yongzhu Chang, Rongsheng Zhang, Jiashu Pu, Guandan Chen, Le Zhang, Yadong Xi, Yijiang Chen, Chang Su
In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.
1 code implementation • 19 Nov 2021 • Luojun Lin, Han Xie, Zhifeng Yang, Zhishu Sun, Wenxi Liu, Yuanlong Yu, WeiJie Chen, Shicai Yang, Di Xie
In this paper, we introduce a novel task, termed as semi-supervised domain generalization, to study how to interact the labeled and unlabeled domains, and establish two benchmarks including a web-crawled dataset, which poses a novel yet realistic challenge to push the limits of existing technologies.
no code implementations • 20 May 2021 • Yongfeng Li, Mingming Zhao, WeiJie Chen, Zaiwen Wen
A general theoretical analysis shows that the solutions generated from a sequence of the constrained optimizations converge to the optimal solution of the LP if the error is controlled properly.
no code implementations • 23 Feb 2021 • WeiJie Chen, Luojun Lin, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang, Wenqi Ren
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
no code implementations • 1 Feb 2021 • WeiJie Chen, Yilu Guo, Shicai Yang, Zhaoyang Li, Zhenxin Ma, Binbin Chen, Long Zhao, Di Xie, ShiLiang Pu, Yueting Zhuang
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
no code implementations • 10 Dec 2020 • Xianfeng Li, WeiJie Chen, Di Xie, Shicai Yang, Peng Yuan, ShiLiang Pu, Yueting Zhuang
However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain.
1 code implementation • 28 Oct 2019 • Yuzhi Zhang, Haidi Wang, WeiJie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E
Materials 3, 023804] and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training.
Computational Physics
no code implementations • 29 May 2019 • Yuangang Pan, WeiJie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
In rank aggregation, preferences from different users are summarized into a total order under the homogeneous data assumption.