Data Augmentation Using GANs for 3D Applications

2020 ◽  
pp. 229-269
Author(s):  
Ioannis Maniadis ◽  
Vassilis Solachidis ◽  
Nicholas Vretos ◽  
Petros Daras
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Property A ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Learning Techniques ◽  
The Subject ◽  
3D Applications

Modern deep learning techniques have proven that they have the capacity to be successful in a wide area of domains and tasks, including applications related to 3D and 2D images. However, their quality depends on the quality and quantity of the data with which models are trained. As the capacity of deep learning models increases, data availability becomes the most significant. To counter this issue, various techniques are utilized, including data augmentation, which refers to the practice of expanding the original dataset with artificially created samples. One approach that has been found is the generative adversarial networks (GANs), which, unlike other domain-agnostic transformation-based methods, can produce diverse samples that belong to a given data distribution. Taking advantage of this property, a multitude of GAN architectures has been leveraged for data augmentation applications. The subject of this chapter is to review and organize implementations of this approach on 3D and 2D imagery, examine the methods that were used, and survey the areas in which they were applied.

scholarly journals Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection

2021 ◽  
Vol 11 (7) ◽  
pp. 3086
Author(s):  
Ricardo Silva Peres ◽  
Miguel Azevedo ◽  
Sara Oleiro Araújo ◽  
Magno Guedes ◽  
Fábio Miranda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Manufacturing Sector ◽  
Data Availability ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Learning Approaches ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Structural Adhesive

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

Data augmentation using a variational autoencoder for estimating property prices

2021 ◽  
Vol 39 (3) ◽  
pp. 408-418 ◽  
Author(s):  
Changro Lee
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Data Augmentation ◽  
Model Performance ◽  
Content Type ◽  
Original Dataset ◽  
Learning Techniques ◽  
Variational Autoencoder ◽  
Price Estimation

PurposePrior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.Design/methodology/approachThe deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.FindingsThe results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.Originality/valueAlthough deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

Gastronomic Algorithms: Artistic and Sensory Exploration of Alain Passard's Michelin Plates in the Manner of Giuseppe Arcimboldo with GANs

Leonardo ◽  
2021 ◽  
pp. 1-11
Author(s):  
Emily L. Spratt
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Artistic Collaboration ◽  
Recent Advances ◽  
Learning Techniques ◽  
Data Scientist ◽  
The Senses ◽  
Computer Programmer

Abstract Although recent advances in artificial intelligence to generate images with deep learning techniques, especially generative adversarial networks (GANs), have offered radically new opportunities for its creative applications, there has been little investigation into its use as a tool to explore the senses beyond vision alone. In an artistic collaboration that brought Chef Alain Passard, art historian and data scientist Emily Spratt, and computer programmer Thomas Fan together, photographs of the three-star Michelin plates from the Parisian restaurant Arpège were used as a springboard to explore the art of culinary presentation in the manner of the Renaissance painter Giuseppe Arcimboldo.

Generative models

2019 ◽  
pp. 162-180
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Deep Learning ◽  
Expectation Maximization ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Learning Context ◽  
Feature Vectors ◽  
Adversarial Networks ◽  
The Subject

This chapter presents an introduction to the important topic of building generative models. These are models that are aimed to understand the variety of a class such as cars or trees. A generative mode should be able to generate feature vectors for instances of the class they represent, and such models should therefore be able to characterize the class with all its variations. The subject is discussed both in a Bayesian and in a deep learning context, and also within a supervised and unsupervised context. This area is related to important algorithms such as k-means clustering, expectation maximization (EM), naïve Bayes, generative adversarial networks (GANs), and variational autoencoders (VAE).

scholarly journals Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bo Pan ◽  
Wei Zheng
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Band ◽  
Computational Models ◽  
Data Augmentation ◽  
Learning Model ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Before And After ◽  
Deep Learning Model

Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.

scholarly journals Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Sajila D. Wickramaratne ◽  
Md.Shaad Mahmud
Keyword(s):  
Deep Learning ◽  
Near Infrared ◽  
Data Augmentation ◽  
Learning Task ◽  
Generative Adversarial Networks ◽  
Functional Near Infrared Spectroscopy ◽  
Learning Classifier ◽  
Adversarial Networks ◽  
Task Classification ◽  
Specific Category

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject’s task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

scholarly journals Hypertrophic Cardiomyopathy Diagnosis Based on Cardiovascular Magnetic Resonance Using Deep Learning Techniques

2021 ◽  
Author(s):  
Danial Sharifrazi ◽  
Roohallah Alizadehsani ◽  
Navid Hoseini Izadi ◽  
Mohamad Roshanzamir ◽  
Afshin Shoeibi ◽  
...  
Keyword(s):  
Cardiovascular Magnetic Resonance ◽  
Deep Learning ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Error Rate ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Original Dataset ◽  
Learning Techniques ◽  
Method Accuracy

Abstract Hypertrophic cardiomyopathy (HCM) can lead to serious cardiac problems. HCM is often diagnosed by an expert using cardiovascular magnetic resonance (CMR) images obtained from patients. In this research, we aimed to develop a deep learning technique to automate HCM diagnosis. CMR images of 37421 healthy and 21846 HCM patients were obtained during two years. Images obtained from female patients form 53% of the collected dataset. The mean and standard deviation of the dataset patients’ age are 48.2 and 19.5 years, respectively. Three experts inspected images and determined whether a case has HCM or not. New data augmentation was used to generate new images by employing color filtering on the existing ones. To classify the augmented images, we used a deep convolutional neural network (CNN). To the best of our knowledge, this is the first time CNN is used for HCM diagnosis. We designed our CNN from scratch to reach acceptable diagnosis accuracy. Comparing the designed algorithm output with the experts’ opinions, the method could achieve accuracy of 95.23%, recall of 97.90%, and specificity of 93.06% on the original dataset. The same performance metrics for the designed algorithm on the augmented dataset were 98.53%, 98.70%, and 95.21%, respectively. We have also experimented with different optimizers (e.g. Adadelta and Adagrad) and other data augmentation methods (e.g. height shift and rotation) to further evaluate the proposed method. Using our data augmentation method, accuracy of 98.53% were achieved which is higher than the best accuracy (95.83%) obtained by the other data augmentation methods which have been evaluated. The upper bound on difference between true error rate and empirical error rate of the proposed method has also been provided in order to present better performance analysis. The advantages of employing the proposed method are elimination of contrast agent and its complications, decreased CMR examination time, lower costs for patients and cardiac imaging centers.

Deep Learning Techniques in Perception of Cancer Diagnosis

2021 ◽  
pp. 1-20
Author(s):  
Anshul ◽  
Raju Kumar
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Generative Adversarial Networks ◽  
Robust Solutions ◽  
Diagnosis And Prognosis ◽  
Adversarial Networks ◽  
Learning Techniques ◽  
Day By Day ◽  
Insight Into ◽  
Timely Treatment

In this era of technology, for effective treatment of patients, clinical experts are getting great support from automated e-healthcare systems. Nowadays, one of the leading reasons of death is cancer. Some common cancers are breast cancer, prostate cancer, lung cancer, skin cancer, brain cancer, and so on. To save human lives from cancer, an effective and timely treatment is required. Many different types of image modalities like CT scan, ultrasound, x-ray, MRI can be used to determine the disease, but traditionally, this was purely dependent on the knowledge and experience of doctors. So, the death rate was quite high and increasing day by day. Machine learning and deep learning are providing robust solutions in this field. There are many deep learning techniques like RNN, CNN, DBN, autoencoders, generative adversarial networks which are providing robust solutions in cancer diagnosis and prognosis so that many human lives can be saved. The objective of this chapter is to give an insight into deep learning techniques in the field of a cancer diagnosis.

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