PM-05Applications of a particle extraction method with deep neural networks using improved auto-encoders to biological ultra-thin section images

Microscopy ◽  
2016 ◽  
Vol 65 (suppl 1) ◽  
pp. i34.1-i34 ◽  
Author(s):  
Shoki Tezuka ◽  
Gen Maeda ◽  
Misuzu Baba ◽  
Norio Baba
Keyword(s):  
Neural Networks ◽  
Thin Section ◽  
Extraction Method ◽  
Deep Neural Networks ◽  
Ultra Thin Section ◽  
Particle Extraction

scholarly journals A novel keyframe extraction method for video classification using deep neural networks

2021 ◽  
Author(s):  
Rukiye Savran Kızıltepe ◽  
John Q. Gan ◽  
Juan José Escobar
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Classification Accuracy ◽  
Extraction Method ◽  
Deep Neural Networks ◽  
Temporal Information ◽  
Video Classification ◽  
Classification Problems ◽  
Keyframe Extraction ◽  
Effective Combination

AbstractCombining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks

2018 ◽  
Author(s):  
Daniel Povey ◽  
Gaofeng Cheng ◽  
Yiming Wang ◽  
Ke Li ◽  
Hainan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Matrix Factorization ◽  
Deep Neural Networks ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix
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