咨询与建议

限定检索结果

文献类型

  • 152 篇 期刊文献
  • 79 篇 会议

馆藏范围

  • 231 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 160 篇 工学
    • 130 篇 计算机科学与技术...
    • 111 篇 软件工程
    • 29 篇 控制科学与工程
    • 23 篇 信息与通信工程
    • 22 篇 光学工程
    • 21 篇 生物工程
    • 19 篇 生物医学工程(可授...
    • 8 篇 电气工程
    • 8 篇 化学工程与技术
    • 6 篇 机械工程
    • 5 篇 电子科学与技术(可...
    • 5 篇 安全科学与工程
    • 4 篇 力学(可授工学、理...
    • 4 篇 仪器科学与技术
    • 4 篇 材料科学与工程(可...
  • 87 篇 理学
    • 57 篇 数学
    • 24 篇 生物学
    • 18 篇 统计学(可授理学、...
    • 17 篇 系统科学
    • 15 篇 物理学
    • 8 篇 化学
  • 31 篇 管理学
    • 19 篇 图书情报与档案管...
    • 13 篇 管理科学与工程(可...
    • 3 篇 工商管理
  • 10 篇 医学
    • 10 篇 临床医学
    • 8 篇 基础医学(可授医学...
    • 8 篇 药学(可授医学、理...
  • 4 篇 法学
    • 4 篇 社会学
  • 3 篇 教育学
    • 3 篇 教育学
  • 2 篇 经济学
    • 2 篇 应用经济学
  • 2 篇 文学
  • 1 篇 艺术学

主题

  • 12 篇 machine learning
  • 8 篇 contrastive lear...
  • 8 篇 training
  • 7 篇 semantics
  • 6 篇 computational li...
  • 5 篇 object detection
  • 5 篇 reinforcement le...
  • 5 篇 task analysis
  • 5 篇 neuroimaging
  • 5 篇 benchmarking
  • 5 篇 stochastic syste...
  • 4 篇 deep learning
  • 4 篇 distillation
  • 4 篇 iterative method...
  • 4 篇 learning algorit...
  • 4 篇 visualization
  • 3 篇 deep neural netw...
  • 3 篇 training data
  • 3 篇 adversarial mach...
  • 3 篇 annotations

机构

  • 81 篇 miit key laborat...
  • 66 篇 college of compu...
  • 28 篇 college of compu...
  • 17 篇 miit key laborat...
  • 13 篇 miit key laborat...
  • 10 篇 collaborative in...
  • 8 篇 college of compu...
  • 6 篇 college of compu...
  • 6 篇 nanjing universi...
  • 5 篇 jd ai research
  • 5 篇 department of el...
  • 5 篇 the college of c...
  • 4 篇 chongqing jiaoto...
  • 4 篇 riken center for...
  • 4 篇 nanyang technolo...
  • 4 篇 department of ma...
  • 4 篇 college of compu...
  • 4 篇 school of comput...
  • 4 篇 school of comput...
  • 4 篇 college of compu...

作者

  • 47 篇 chen songcan
  • 23 篇 li piji
  • 23 篇 huang sheng-jun
  • 18 篇 huang feihu
  • 18 篇 zhang daoqiang
  • 16 篇 sheng-jun huang
  • 16 篇 liang dong
  • 15 篇 songcan chen
  • 12 篇 tan xiaoyang
  • 10 篇 geng chuanxing
  • 9 篇 wang xinrui
  • 9 篇 daoqiang zhang
  • 7 篇 li shao-yuan
  • 7 篇 wei mingqiang
  • 7 篇 li zhongnian
  • 7 篇 ming-kun xie
  • 6 篇 xie ming-kun
  • 6 篇 wang renzhi
  • 6 篇 li weikai
  • 6 篇 tao lue

语言

  • 218 篇 英文
  • 11 篇 其他
  • 5 篇 中文
检索条件"机构=Miit Key Laboratory of Pattern Analysis and Machine Intelligence"
231 条 记 录,以下是161-170 订阅
排序:
With false friends like these, who can notice mistakes?
arXiv
收藏 引用
arXiv 2020年
作者: Tao, Lue Feng, Lei Yi, Jinfeng Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science Chongqing University China JD AI Research
Adversarial examples crafted by an explicit adversary have attracted significant attention in machine learning. However, the security risk posed by a potential false friend has been largely overlooked. In this paper, ... 详细信息
来源: 评论
Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization
arXiv
收藏 引用
arXiv 2024年
作者: Cao, Meng Chen, Songcan The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China
Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their marginal distributions, irrespective of ...
来源: 评论
Open-set label noise can improve robustness against inherent label noise
arXiv
收藏 引用
arXiv 2021年
作者: Wei, Hongxin Tao, Lue Xie, Renchunzi An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT key Laboratory of Pattern Analysis and Machine Intelligence China
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-se... 详细信息
来源: 评论
Deep robust multilevel semantic cross-modal hashing
arXiv
收藏 引用
arXiv 2020年
作者: Song, Ge Zhao, Jun Tan, Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Miit Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanyang Technological University
Hashing based cross-modal retrieval has rec made significant progress. But straightfor embedding data from different modalities in joint Hamming space will inevitably produce codes due to the intrinsic modality discre... 详细信息
来源: 评论
Open-set label noise can improve robustness against inherent label noise  21
Open-set label noise can improve robustness against inherent...
收藏 引用
Proceedings of the 35th International Conference on Neural Information Processing Systems
作者: Hongxin Wei Lue Tao Renchunzi Xie Bo An School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China and MIIT key Laboratory of Pattern Analysis and Machine Intelligence China
Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-se...
来源: 评论
PIE: Physics-inspired Low-light Enhancement
arXiv
收藏 引用
arXiv 2024年
作者: Liang, Dong Xu, Zhengyan Li, Ling Wei, Mingqiang Chen, Songcan MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing Universily of Aeronautics Astronautics Shenzhen Research Institute China
In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods oft... 详细信息
来源: 评论
Relative Difficulty Distillation for Semantic Segmentation
arXiv
收藏 引用
arXiv 2024年
作者: Liang, Dong Sun, Yue Du, Yun Chen, Songcan Huang, Sheng-Jun MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Nanjing University of Aeronautics Astronautics Shenzhen Research Institute China
Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the tea... 详细信息
来源: 评论
Adaptive Federated Minimax Optimization with Lower Complexities
arXiv
收藏 引用
arXiv 2022年
作者: Huang, Feihu Wang, Xinrui Li, Junyi Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States
Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve the distributed minimax problems. Howe... 详细信息
来源: 评论
Forgetting, Ignorance or Myopia: Revisiting key Challenges in Online Continual Learning
arXiv
收藏 引用
arXiv 2024年
作者: Wang, Xinrui Geng, Chuanxing Wan, Wenhai Li, Shao-Yuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China School of Computer Science and Technology Huazhong University of Science and Technology China
Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the catastrophic forgetting ... 详细信息
来源: 评论
Dense Face Detection via High-level Context Mining
Dense Face Detection via High-level Context Mining
收藏 引用
International Conference on Automatic Face and Gesture Recognition
作者: Qixiang Geng Dong Liang Huiyu Zhou Liyan Zhang Han Sun Ningzhong Liu MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Informatics University of Leicester
The appearance degradation caused by low resolution is the core problem of small face detection. Therefore, a natural approach is to assemble information from the context. This paper focuses on how to use high-level c... 详细信息
来源: 评论