scholarly journals Transfer Learning Using Rotated Image Data to Improve Deep Neural Network Performance

2014 ◽  
pp. 290-300 ◽  
Author(s):  
Telmo Amaral ◽  
Luís M. Silva ◽  
Luís A. Alexandre ◽  
Chetak Kandaswamy ◽  
Joaquim Marques de Sá ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Performance ◽  
Deep Neural Network ◽  
Image Data

PH-0287: Transfer learning and Deep Neural Network for lung and heart dose prediction in breast treatments

2020 ◽  
Vol 152 ◽  
pp. S146-S147
Author(s):  
J. Perez-Alija ◽  
P. Gallego ◽  
M. Lizondo ◽  
J. Nuria ◽  
A. Latorre-Musoll ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Deep Neural Network ◽  
Heart Dose ◽  
Dose Prediction

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

scholarly journals Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoping Guo
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Transfer Learning ◽  
Deep Neural Network ◽  
Mutation Detection ◽  
Video Retrieval ◽  
Flow Analysis ◽  
Key Frame Extraction ◽  
Sports Video ◽  
Key Frame

Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses convolutional neural networks to extract the underlying feature information of images to build indexes and achieves similarity retrieval of video feature vectors according to certain similarity measure algorithms. In this paper, by studying the characteristics of sports videos, we propose the histogram difference method based on using transfer learning and the four-step method based on block matching for mutation detection and fading detection of video shots, respectively. By adaptive thresholding, regions with large frame difference changes are marked as candidate regions for shots, and then the shot boundaries are determined by mutation detection algorithm. Combined with the characteristics of sports video, this paper proposes a key frame extraction method based on clustering and optical flow analysis, and experimental comparison with the traditional clustering method. In addition, this paper proposes a key frame extraction algorithm based on clustering and optical flow analysis for key frame extraction of sports video. The algorithm effectively removes the redundant frames, and the extracted key frames are more representative. Through extensive experiments, the keyword fuzzy finding algorithm based on improved deep neural network and ontology semantic expansion proposed in this paper shows a more desirable retrieval performance, and it is feasible to use this method for video underlying feature extraction, annotation, and keyword finding, and one of the outstanding features of the algorithm is that it can quickly and effectively retrieve the desired video in a large number of Internet video resources, reducing the false detection rate and leakage rate while improving the fidelity, which basically meets people’s daily needs.

Analysis of transfer learning for deep neural network based plant classification models

2019 ◽  
Vol 158 ◽  
pp. 20-29 ◽  
Author(s):  
Aydin Kaya ◽  
Ali Seydi Keceli ◽  
Cagatay Catal ◽  
Hamdi Yalin Yalic ◽  
Huseyin Temucin ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Deep Neural Network ◽  
Classification Models
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