SS-HCNN : semi-supervised hierarchical convolutional neural network for image classification
The availability of large-scale annotated data and uneven separability of different data categories become two major impediments of deep learning for image classification. In this paper, we present a Semi-Supervised Hierarchical Convolutional Neural Network (SS-HCNN) to address these two challenges....
Main Authors: | Chen, Tao, Lu, Shijian, Fan, Jiayuan |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/143029 |
Similar Items
-
Deep Learning-Based HCNN and CRF-RRNN Model for Brain Tumor Segmentation
by: Wu Deng, et al.
Published: (2020-01-01) -
A semi-supervised convolutional neural network based on subspace representation for image classification
by: Bernardo B. Gatto, et al.
Published: (2020-06-01) -
Semi-supervised tooth instance segmentation
by: Ling, Zijie
Published: (2024) -
Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network
by: Huihui Song, et al.
Published: (2024-01-01) -
Semi-Supervised Training of Transformer and Causal Dilated Convolution Network with Applications to Speech Topic Classification
by: Jinxiang Zeng, et al.
Published: (2021-06-01)