Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation
M Hashemi - Journal of Big Data, 2019 - Springer
The input to a machine learning model is a one-dimensional feature vector. However, in
recent learning models, such as convolutional and recurrent neural networks, two-and three …
recent learning models, such as convolutional and recurrent neural networks, two-and three …
A survey of deep network techniques all classifiers can adopt
Deep neural networks (DNNs) have introduced novel and useful tools to the machine
learning community. Other types of classifiers can potentially make use of these tools as well …
learning community. Other types of classifiers can potentially make use of these tools as well …
Incremental boosting convolutional neural network for facial action unit recognition
Recognizing facial action units (AUs) from spontaneous facial expressions is still a
challenging problem. Most recently, CNNs have shown promise on facial AU recognition …
challenging problem. Most recently, CNNs have shown promise on facial AU recognition …
A comprehensive study on center loss for deep face recognition
Deep convolutional neural networks (CNNs) trained with the softmax loss have achieved
remarkable successes in a number of close-set recognition problems, eg object recognition …
remarkable successes in a number of close-set recognition problems, eg object recognition …
Hybrid neural network with cost-sensitive support vector machine for class-imbalanced multimodal data
KH Kim, SY Sohn - Neural Networks, 2020 - Elsevier
Although deep learning exhibits advantages in various applications involving multimodal
data, it cannot effectively solve the class-imbalance problem. Herein, we propose a hybrid …
data, it cannot effectively solve the class-imbalance problem. Herein, we propose a hybrid …
Sparse representations for facial expressions recognition via l1 optimization
S Zafeiriou, M Petrou - 2010 IEEE Computer Society …, 2010 - ieeexplore.ieee.org
In this paper, the principles of sparse signal representation theory are explored in order to
perform facial expressions recognition from frontal views. Motivated by the success such …
perform facial expressions recognition from frontal views. Motivated by the success such …
[PDF][PDF] A deep learning approach combining CNN and Bi-LSTM with SVM classifier for Arabic sentiment analysis
O Alharbi - International Journal of Advanced Computer Science …, 2021 - researchgate.net
Deep learning models have recently been proven to be successful in various natural
language processing tasks, including sentiment analysis. Conventionally, a deep learning …
language processing tasks, including sentiment analysis. Conventionally, a deep learning …
Convolutional support vector models: Prediction of coronavirus disease using chest x-rays
The disease caused by the new coronavirus (COVID-19) has been plaguing the world for
months and the number of cases are growing more rapidly as the days go by. Therefore …
months and the number of cases are growing more rapidly as the days go by. Therefore …