scholarly journals RESEARCH IN-DEPTH TRAINING OF NEURAL NETWORKS

2020 ◽  
Vol 70 (2) ◽  
pp. 234-238
Author(s):  
K.S. Imanbaev ◽  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Back Propagation ◽  
Fine Tuning ◽  
Current State ◽  
Large Numbers ◽  
History Of ◽  
Propagation Of Errors

Currently, deep learning of neural networks is one of the most popular methods for speech recognition, natural language processing, and computer vision. The article reviews the history of deep learning of neural networks and the current state in General. We consider algorithms for training neural networks used for deep training of neural networks, followed by fine-tuning using the method of back propagation of errors. Neural networks with large numbers of hidden layers, frequently occurring and disappearing gradients are very difficult to train. In this paper, we consider methods that successfully implement training of neural networks with large numbers of layers (more than one hundred) and vanishing gradients. A review of well-known libraries used for successful deep learning of neural networks is conducted.

scholarly journals A Study of The Convolutional Neural Networks Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Ahmed Shamsaldin ◽  
Polla Fattah ◽  
Tarik Rashid ◽  
Nawzad Al-Salihi
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Scene Labeling ◽  
Real World Problems

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

scholarly journals Convolution Neural Network: A Shallow Dive in to Deep Neural Net Technology

2019 ◽  
Vol 8 (2S7) ◽  
pp. 487-495
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Background Information ◽  
Convolution Neural Networks

It is always beneficial to reassess the previously done work to create interest and develop understanding about the subject in importance. In computer vision, to perform the task of feature extraction, classification or segmentation, measurement and assessment of image structures (medical images, natural images etc.) is to be done very efficiently. In the field of image processing numerous techniques are available, but it is very difficult to perform these tasks due to noise and other variable artifacts. Various Deep machine learning algorithms are used to perform complex task of recognition and computer vision. Recently Convolutional Neural Networks (CNNs-back bone of numerous deep learning algorithms) have shown state of the art performance in high level computer vision tasks, such as object detection, object recognition, classification, machine translation, semantic segmentation, speech recognition, scene labelling, medical imaging, robotics and control, , natural language processing (NLP), bio-informatics, cybersecurity, and many others. Convolution neural networks is the attempt to combine mathematics to computer science with icing of biology on it. CNNs work in two parts. The first part is mathematics that supports feature extraction and second part is about classification and prediction at pixel level. This review is intended for those who want to grab the complete knowledge about CNN, their development form ancient age to modern state of art system of deep learning system. This review paper is organized in three steps: in the first step introduction about the concept is given along with necessary background information. In the second step other highlights and related work proposed by various authors is explained. Third step is the complete layer wise architecture of convolution networks. The last section is followed by detailed discussion on improvements, and challenges on these deep learning techniques. Most papers consider for this review are later than 2012 from when the history of convolution neural networks and deep learning begins

scholarly journals A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications

2022 ◽  
Author(s):  
Ms. Aayushi Bansal ◽  
Dr. Rewa Sharma ◽  
Dr. Mamta Kathuria
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Medical Science ◽  
Real Life ◽  
Survey Paper ◽  
Augmentation Techniques ◽  
Comprehensive Survey

Recent advancements in deep learning architecture have increased its utility in real-life applications. Deep learning models require a large amount of data to train the model. In many application domains, there is a limited set of data available for training neural networks as collecting new data is either not feasible or requires more resources such as in marketing, computer vision, and medical science. These models require a large amount of data to avoid the problem of overfitting. One of the data space solutions to the problem of limited data is data augmentation. The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network. This saves the cost and time consumption required to collect new data for the training of deep neural networks by augmenting available data. This also regularizes the model and improves its capability of generalization. The need for large datasets in different fields such as computer vision, natural language processing, security and healthcare is also covered in this survey paper. The goal of this paper is to provide a comprehensive survey of recent advancements in data augmentation techniques and their application in various domains.

Image Captioning Using Deep Learning

2021 ◽  
pp. 381-395
Author(s):  
Bhavana D. ◽  
K. Chaitanya Krishna ◽  
Tejaswini K. ◽  
N. Venkata Vikas ◽  
A. N. V. Sahithya
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Input Image ◽  
The Other ◽  
Image Caption

The task of image caption generator is mainly about extracting the features and ongoings of an image and generating human-readable captions that translate the features of the objects in the image. The contents of an image can be described by having knowledge about natural language processing and computer vision. The features can be extracted using convolution neural networks which makes use of transfer learning to implement the exception model. It stands for extreme inception, which has a feature extraction base with 36 convolution layers. This shows accurate results when compared with the other CNNs. Recurrent neural networks are used for describing the image and to generate accurate sentences. The feature vector that is extracted by using the CNN is fed to the LSTM. The Flicker 8k dataset is used to train the network in which the data is labeled properly. The model will be able to generate accurate captions that nearly describe the activities carried in the image when an input image is given to it. Further, the authors use the BLEU scores to validate the model.

Counterfactual Autoencoder for Unsupervised Semantic Learning

2020 ◽  
pp. 720-736
Author(s):  
Saad Sadiq ◽  
Mei-Ling Shyu ◽  
Daniel J. Feaster
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Speech Processing ◽  
Semantic Learning ◽  
Novel Approach ◽  
Training Samples ◽  
Latent Space

Deep Neural Networks (DNNs) are best known for being the state-of-the-art in artificial intelligence (AI) applications including natural language processing (NLP), speech processing, computer vision, etc. In spite of all recent achievements of deep learning, it has yet to achieve semantic learning required to reason about the data. This lack of reasoning is partially imputed to the boorish memorization of patterns and curves from millions of training samples and ignoring the spatiotemporal relationships. The proposed framework puts forward a novel approach based on variational autoencoders (VAEs) by using the potential outcomes model and developing the counterfactual autoencoders. The proposed framework transforms any sort of multimedia input distributions to a meaningful latent space while giving more control over how the latent space is created. This allows us to model data that is better suited to answer inference-based queries, which is very valuable in reasoning-based AI applications.

scholarly journals Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning

2020 ◽  
Author(s):  
Jhonatan Souza ◽  
Tiago De Oliveira ◽  
Claudemir Casa ◽  
André Ortoncelli
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Social Network ◽  
Transfer Learning ◽  
Language Processing ◽  
Automatic Detection ◽  
Malar Rash ◽  
Popular Approach ◽  
Starting Point

This work presents an approach to the automatic detection of Butterfly Malar Rash (BMR) in images. BMR is a Lupus symptom characterized by a reddish facial rash that appears symmetrically in the cheeks and the back of the nose. The proposed approach is based on Transfer Learning, a popular approach in Deep Learning that consists in the use of pre-trained models as the starting point for computer vision and natural language processing tasks. To perform the experiments, a database was created with images manually collected from the Instagram social network, searching for images with #butterflyrash. We evaluated the proposed approach with eight Convolutional Neural Networks (CNN) architecture. The experimental results are good results, with a precision of up to 0.957.

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

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