scholarly journals Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation

2018 ◽  
Vol 13 (2) ◽  
pp. 437-459 ◽  
Author(s):  
Panayiota Touloupou ◽  
Naif Alzahrani ◽  
Peter Neal ◽  
Simon E. F. Spencer ◽  
Trevelyan J. McKinley
Keyword(s):  
Large Scale ◽  
Model Comparison ◽  
Data Augmentation ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Mask R-CNN with data augmentation for food detection and recognition

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Large Scale ◽  
Data Augmentation ◽  
State Of The Art ◽  
Projective Representation ◽  
Large Scale Data ◽  
Food Detection ◽  
Food Recognition ◽  
Data Driven Approach ◽  
Scale Data ◽  
Detection And Recognition

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics<br>

scholarly journals Mask R-CNN with data augmentation for food detection and recognition

2020 ◽  
Author(s):  
Than Le
Keyword(s):  
Large Scale ◽  
Data Augmentation ◽  
State Of The Art ◽  
Projective Representation ◽  
Large Scale Data ◽  
Food Detection ◽  
Food Recognition ◽  
Data Driven Approach ◽  
Scale Data ◽  
Detection And Recognition

In this paper, we focus on simple data-driven approach to solve deep learning based on implementing the Mask R-CNN module by analyzing deeper manipulation of datasets. We firstly approach to affine transformation and projective representation to data augmentation analysis in order to increasing large-scale data manually based on the state-of-the-art in views of computer vision. Then we evaluate our method concretely by connection our datasets by visualization data and completely in testing to many methods to understand intelligent data analysis in object detection and segmentation by using more than 5000 image according to many similar objects. As far as, it illustrated efficiency of small applications such as food recognition, grasp and manipulation in robotics<br>

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data
Sign in / Sign up

Export Citation Format

Share Document