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.

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 Multilingual Automatic Term Extraction in Low-Resource Domains

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
NGOC TAN LE ◽  
Fatiha Sadat
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Large Scale ◽  
Data Augmentation ◽  
Shared Task ◽  
Low Resource ◽  
Term Extraction ◽  
Augmentation Techniques ◽  
Automatic Term Extraction ◽  
The Neural Networks

With the emergence of the neural networks-based approaches, research on information extraction has benefited from large-scale raw texts by leveraging them using pre-trained embeddings and other data augmentation techniques to deal with challenges and issues in Natural Language Processing tasks. In this paper, we propose an approach using sequence-to-sequence neural networks-based models to deal with term extraction for low-resource domain. Our empirical experiments, evaluating on the multilingual ACTER dataset provided in the LREC-TermEval 2020 shared task on automatic term extraction, proved the efficiency of deep learning approach, in the case of low-data settings, for the automatic term extraction task.

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 Deep Learning for the Russian Language

2020 ◽  
pp. 465-481
Author(s):  
Ekaterina Artemova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Linguistic Diversity ◽  
Real Life ◽  
Russian Language ◽  
Human Vision ◽  
The Russian Language

AbstractDeep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence.While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.

scholarly journals Detection of citrus leaf diseases using a deep learning technique

2021 ◽  
Vol 11 (2) ◽  
pp. 1719
Author(s):  
Ahmed R. Luaibi ◽  
Tariq M. Salman ◽  
Abbas Hussein Miry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Data Augmentation ◽  
Original Data ◽  
Training Images ◽  
Learning Technique ◽  
Data Points ◽  
Citrus Leaves

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

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

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.

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