Layout Error Correction Using Deep Neural Networks

2018 ◽  
Author(s):  
Srie Raam Mohan ◽  
Syed Saqib Bukhari ◽  
Andreas Dengel
Keyword(s):  
Neural Networks ◽  
Error Correction ◽  
Deep Neural Networks

scholarly journals ERDNN: Error-Resilient Deep Neural Networks With a New Error Correction Layer and Piece-Wise Rectified Linear Unit

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158702-158711
Author(s):  
Muhammad Salman Ali ◽  
Tauhid Bin Iqbal ◽  
Kang-Ho Lee ◽  
Abdul Muqeet ◽  
Seunghyun Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Error Correction ◽  
Deep Neural Networks ◽  
Error Resilient ◽  
Rectified Linear Unit

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

2020 ◽  
Vol 35 (12) ◽  
pp. 1987-2008 ◽  
Author(s):  
Han Wang ◽  
Haixian Zhang ◽  
Junjie Hu ◽  
Ying Song ◽  
Sen Bai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Error Correction ◽  
Deep Neural Networks ◽  
Dose Prediction

scholarly journals Korean Grammatical Error Correction Based on Transformer with Copying Mechanisms and Grammatical Noise Implantation Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2658
Author(s):  
Myunghoon Lee ◽  
Hyeonho Shin ◽  
Dabin Lee ◽  
Sung-Pil Choi
Keyword(s):  
Neural Networks ◽  
Error Correction ◽  
Deep Neural Networks ◽  
Language Models ◽  
Grammatical Errors ◽  
Grammatical Error

Grammatical Error Correction (GEC) is the task of detecting and correcting various grammatical errors in texts. Many previous approaches to the GEC have used various mechanisms including rules, statistics, and their combinations. Recently, the performance of the GEC in English has been drastically enhanced due to the vigorous applications of deep neural networks and pretrained language models. Following the promising results of the English GEC tasks, we apply the Transformer with Copying Mechanism into the Korean GEC task by introducing novel and effective noising methods for constructing Korean GEC datasets. Our comparative experiments showed that the proposed system outperforms two commercial grammar check and other NMT-based models.

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
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