Data Augmentation, Internal Representation, and Unsupervised Learning Comment

被引:3
|
作者
Kelly, Brandon C. [1 ]
机构
[1] Harvard Smithsonian Ctr Astrophys, Cambridge, MA 02138 USA
关键词
D O I
10.1198/jcgs.2011.203c
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
引用
收藏
页码:584 / 591
页数:8
相关论文
共 50 条
  • [1] Data Augmentation, Internal Representation, and Unsupervised Learning
    Wu, Ying Nian
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2011, 20 (03) : 581 - 583
  • [2] Disturbed Augmentation Invariance for Unsupervised Visual Representation Learning
    Cheng, Haoyang
    Li, Hongliang
    Wu, Qingbo
    Qiu, Heqian
    Zhang, Xiaoliang
    Meng, Fanman
    Zhao, Taijin
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 33 (11) : 6924 - 6938
  • [3] Unsupervised learning using topological data augmentation
    Balabanov, Oleksandr
    Granath, Mats
    PHYSICAL REVIEW RESEARCH, 2020, 2 (01):
  • [4] Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
    Wang, Chunchun
    Lv, Shu
    APPLIED SCIENCES-BASEL, 2024, 14 (07):
  • [5] A data augmentation model integrating supervised and unsupervised learning for recommendation
    Chen, Jiaying
    Zhu, Zhongrui
    Li, Haoyang
    Jiang, Wanlong
    Jeon, Gwanggil
    Qian, Yurong
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [6] TabReformer: Unsupervised Representation Learning for Erroneous Data Detection
    Nashaat, Mona
    Ghosh, Aindrila
    Miller, James
    Quader, Shaikh
    ACM/IMS Transactions on Data Science, 2021, 2 (03):
  • [7] Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
    Lowell, David
    Howard, Brian E.
    Lipton, Zachary C.
    Wallace, Byron C.
    2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), 2021, : 4992 - 5001
  • [8] SeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Learning
    Qian, Qi
    Xu, Yuanhong
    Hui, Juhua
    COMPUTER VISION - ECCV 2024, PT LXXVIII, 2025, 15136 : 1 - 17
  • [9] GMNI: Achieve good data augmentation in unsupervised graph contrastive learning
    Xiong, Xin
    Wang, Xiangyu
    Yang, Suorong
    Shen, Furao
    Zhao, Jian
    NEURAL NETWORKS, 2025, 181
  • [10] Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning
    Hsu, Chia-Yi
    Chen, Pin-Yu
    Lu, Songtao
    Liu, Sijia
    Yu, Chia-Mu
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 6926 - 6934