Design Domain-Adaptive Generative Performance Optimization Using Designable Data Augmentation

2021 ◽  
Vol 26 (3) ◽  
pp. 208-220
Author(s):  
Minsung Kang ◽  
Yeongmin Yoo ◽  
Changkyu Park ◽  
Jongsoo Lee
Keyword(s):  
Performance Optimization ◽  
Data Augmentation ◽  
Design Domain

scholarly journals Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4641
Author(s):  
Boseon Hong ◽  
Bongjae Kim
Keyword(s):  
Performance Optimization ◽  
Character Recognition ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Android Application ◽  
Recognition Model ◽  
Intelligence Models ◽  
Augmentation Techniques ◽  
Artificial Intelligence Models ◽  
Optimized Model

Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

Performance Optimization of Semi-Custom Memory in 7nm FPGA

2020 ◽  
Author(s):  
Sourabh Aditya Swarnkar ◽  
Mohammad Anees ◽  
Kumar Rahul ◽  
Santosh Yachareni
Keyword(s):  
Performance Optimization
Sign in / Sign up

Export Citation Format

Share Document