Deep active sampling with self-supervised learning

被引：2
|
作者：
Shi, Haochen [1 ,2 ]
Zhou, Hui [2 ]
机构：
[1] Nanjing Univ Aeronut & Astronaut, Coll Elect & Informat Engn, Nanjing 211106, Peoples R China
[2] Nanjing Univ Aeronut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China
来源：
FRONTIERS OF COMPUTER SCIENCE | 2023年 / 17卷 / 04期
关键词：
D O I：
10.1007/s11704-022-2189-z
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
引用
收藏
页数：3
相关论文
共 50 条
  • [21] AN ITERATIVE FRAMEWORK FOR SELF-SUPERVISED DEEP SPEAKER REPRESENTATION LEARNING
    Cai, Danwei
    Wang, Weiqing
    Li, Ming
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 6728 - 6732
  • [22] Reducing Label Effort: Self-Supervised meets Active Learning
    Bengar, Javad Zolfaghari
    van de Weijer, Joost
    Twardowski, Bartlomiej
    Raducanu, Bogdan
    2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), 2021, : 1631 - 1639
  • [23] Self-Supervised Image Quality Assessment through Active Learning
    Yu, Yunchao
    Sang, Qingbing
    PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024, 2024, : 315 - 319
  • [24] Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
    Li, Shunkai
    Xue, Fei
    Wang, Xin
    Yan, Zike
    Zha, Hongbin
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 2851 - 2860
  • [25] Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
    Sauder, Jonathan
    Sievers, Bjarne
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [26] Self-supervised deep learning to assess breast cancer risk
    Woodard, Anna
    Omoleye, Olasubomi J.
    Gupta, Rachna
    Zhao, Fangyuan
    Koripelly, Aarthi
    Foster, Ian
    Chard, Kyle
    Yoshimatsu, Toshio F.
    Zheng, Yonglan
    Huo, Dezheng
    Olopade, Olufunmilayo I.
    CANCER RESEARCH, 2022, 82 (12)
  • [27] Online Self-Supervised Deep Learning for Intrusion Detection Systems
    Nakip, Mert
    Gelenbe, Erol
    IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2024, 19 : 5668 - 5683
  • [28] NVST Image Denoising Based on Self-Supervised Deep Learning
    Lu Xianwei
    Liu Hui
    Shang Zhenhong
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (06)
  • [29] SeSe-Net: Self-Supervised deep learning for segmentation
    Zeng Zeng
    Yang Xulei
    Yu Qiyun
    Yao Meng
    Zhang Le
    PATTERN RECOGNITION LETTERS, 2019, 128 : 23 - 29
  • [30] Intrinsically Motivated Self-Supervised Deep Sensorimotor Learning for Grasping
    Takahashi, Takeshi
    Lanighan, Michael W.
    Grupen, Roderic A.
    2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2018, : 3496 - 3502
12345