Fine-Grained Categorization Using a Mixture of Transfer Learning Networks

被引:0
|
作者
Firsching, Justin [1 ]
Hashem, Sherif [1 ,2 ]
机构
[1] State Univ New York Polytech Inst, Utica, NY 13502 USA
[2] Cairo Univ, Giza 12316, Egypt
关键词
Combinations of neural networks; Mixture of experts; Transfer learning; Convolutional neural network; Computer vision; Classification; Fine-grained categorization; Artificial intelligence; Deep learning;
D O I
10.1007/978-3-030-89880-9_11
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we apply a mixture of experts approach to further enhance the accuracy of transfer learning networks on a fine-grained categorization problem, expanding on the work of Firsching and Hashem. Mixture of experts approaches may help to improve accuracy on categorization problems. Likewise, transfer learning is a highly effective technique for solving problems in machine learning of varying complexities. We here illustrate that mixtures of trained transfer learning networks, when applied properly, may further improve categorization accuracy.
引用
收藏
页码:151 / 158
页数:8
相关论文
共 50 条
  • [1] Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization
    Zhang, Lianbo
    Huang, Shaoli
    Liu, Wei
    Tao, Dacheng
    [J]. 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 8330 - 8339
  • [2] Hierarchical deep transfer learning for fine-grained categorization on micro datasets
    Wang, Ronggui
    Yao, Xuchen
    Yang, Juan
    Xue, Lixia
    Hu, Min
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2019, 62 : 129 - 139
  • [3] Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
    Cui, Yin
    Song, Yang
    Sun, Chen
    Howard, Andrew
    Belongie, Serge
    [J]. 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 4109 - 4118
  • [4] Channel Interaction Networks for Fine-Grained Image Categorization
    Gao, Yu
    Han, Xintong
    Wang, Xun
    Huang, Weilin
    Scott, Matthew R.
    [J]. THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 10818 - 10825
  • [5] Semi-Supervised Fine-Grained Image Categorization Using Transfer Learning With Hierarchical Multi-Scale Adversarial Networks
    Chen, Peng
    Li, Peng
    Li, Qing
    Zhang, Dezheng
    [J]. IEEE ACCESS, 2019, 7 : 118650 - 118668
  • [6] Learning sequentially diversified representations for fine-grained categorization
    Zhang, Lianbo
    Huang, Shaoli
    Liu, Wei
    [J]. PATTERN RECOGNITION, 2022, 121
  • [7] Fine-Grained Categorization by Alignments
    Gavves, E.
    Fernando, B.
    Snoek, C. G. M.
    Smeulders, A. W. M.
    Tuytelaars, T.
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2013, : 1713 - 1720
  • [8] Plant Leaf Diseases Fine-Grained Categorization Using Convolutional Neural Networks
    Wu, Yang
    Feng, Xian
    Chen, Guojun
    [J]. IEEE ACCESS, 2022, 10 : 41087 - 41096
  • [9] Arabic Fine-Grained Opinion Categorization Using Discriminative Machine Learning Technique
    Touati, Imen
    Graja, Marwa
    Ellouze, Mariem
    Belguith, Lamia Hadrich
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT SYSTEMS AND INFORMATICS 2016, 2017, 533 : 104 - 113
  • [10] Category attention transfer for efficient fine-grained visual categorization
    Liao, Qiyu
    Wang, Dadong
    Xu, Min
    [J]. PATTERN RECOGNITION LETTERS, 2022, 153 : 10 - 15