Optimization of Deep Learning Algorithms for Object Classification

被引：0
|
作者：
Horvath, Andras [1 ]
机构：
[1] Pazmany Peter Catholic Univ, Fac Informat Technol & Bion, Budapest, Hungary
来源：
EIGHTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2016) | 2017年 / 10225卷
关键词：
Deep learning; Convolutional Neural Networks; Embedded systems; Linearization;
D O I：
10.1117/12.2266403
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Deep learning is currently the state of the art algorithm for image classification. The complexity of these feed-forward neural networks have overcome a critical point, resulting algorithmic breakthroughs in various fields. On the other hand their complexity makes them executable in tasks, where High-throughput computing powers are available. The optimization of these networks -considering computational complexity and applicability on embedded systems- has not yet been studied and investigated in details. In this paper I show some examples how this algorithms can be optimized and accelerated on embedded systems.
引用
收藏
页数：5
相关论文
共 50 条
  • [1] Photometric space object classification via deep learning algorithms
    Liu, Tong
    Schreiber, K. Ulrich
    [J]. ACTA ASTRONAUTICA, 2021, 185 : 161 - 169
  • [2] Deep learning optimization for small object classification in lensfree holographic microscopy
    Potter, Colin J.
    Sreevatsan, Shriniketh
    McLeod, Euan
    [J]. Optics Express, 2024, 32 (20) : 35062 - 35081
  • [3] Deep Learning for Logic Optimization Algorithms
    Haaswijk, Winston
    Collins, Edo
    Seguin, Benoit
    Soeken, Mathias
    Kaplan, Frederic
    Susstrunk, Sabine
    De Micheli, Giovanni
    [J]. 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2018,
  • [4] A Comparison of Optimization Algorithms for Deep Learning
    Soydaner, Derya
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (13)
  • [5] Spectral Classification Based on Deep Learning Algorithms
    Xu, Laixiang
    Xie, Jun
    Cai, Fuhong
    Wu, Jingjin
    [J]. ELECTRONICS, 2021, 10 (16)
  • [6] Comprehending Object Detection by Deep Learning Methods and Algorithms
    Priyanka, Mallineni
    Lavanya, Kotapati
    Charan Sai, K.
    Rohit, Kavuri
    Bano, Shahana
    [J]. Lecture Notes on Data Engineering and Communications Technologies, 2022, 126 : 523 - 537
  • [7] Understanding Deep Learning Algorithms for Object Detection and Recognition
    Suriya, S.
    Rajasekar, Rajesh Harinarayanan
    Shalinie, S. Mercy
    [J]. 2019 11TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC 2019), 2019, : 79 - 85
  • [8] Computational Comparison of Deep Learning Algorithms for Object Detection
    Balafas, Vasileios
    Ploskas, Nikolaos
    [J]. 25TH PAN-HELLENIC CONFERENCE ON INFORMATICS WITH INTERNATIONAL PARTICIPATION (PCI2021), 2021, : 79 - 83
  • [9] Object Detection Algorithms Based on Deep Learning and Transformer
    Fu, Miaomiao
    Deng, Miaolei
    Zhang, Dexian
    [J]. Computer Engineering and Applications, 2023, 59 (01): : 37 - 48
  • [10] Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos
    Saikrishnan, Venkatesan
    Karthikeyan, Mani
    [J]. ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2023, 13 (05) : 11747 - 11752
12345