Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

Caramihale, Traian; Popescu, Dan; Ichim, Loretta

doi:10.3390/sym10090414

Cited by 21 publications

(13 citation statements)

References 35 publications

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“…The discriminator tries to distinguish between real data and fake (artificially generated) data generated by the generator network as shown in Figure 2. The mission GANs models that generator network is to try fooling the discriminator network and the discriminator network tries to fight from being fooled [24][25][26][27]. Figure 2.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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“…The GANs model is utilised to increase the size of data whose actual classification values are hardly secured as well as to generate certain data types such as image, video, and voice. Caramihale, Popescu, and Ichim (2018) and Luo and Lu (2018) augmented key facial expression data and brain wave data by utilising the GANs. Their finding indicates that as augmented data are utilised for training an emotional lassification model, the model's accuracy is enhanced.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

Chang

Jun

2019

Journal of Asian Architecture and Building Engineering

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In architectural planning and initial designing process, it is critical for architects to recognise users' emotional responses toward design alternatives. Since Building Information Modelling and related technologies focuses on physical elements of the building, a model which suggests decision-makers' subjective affection is strongly required. In this regard, this paper proposes an electroencephalography (EEG)-based hybrid deep-learning model to recognise the emotional responses of users towards given architectural design. The hybrid model consists of generative adversarial networks (GANs) for EEG data augmentation and an EEGbased deep-learning classification model for EEG classification. In the field of architecture, a previous study has developed an EEG-based deep-learning classification model that can recognise the emotional responses of subjects towards design alternatives. This approach seems to suggest a possible method of evaluating design alternatives in a quantitative manner. However, because of the limitations of EEG data, it is difficult to train the model, which leads to the limited utilisation of the model. In this regard, this study constructs GANs, which consists of a generator and discriminator, for EEG data augmentation. The proposed hybrid model may provide a method of developing supportive and evaluative environments in planning, design, and post-occupancy evaluation for decision-makers.

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“…Their own voice can then be used to communicate, instead of a generic computer voice synthesizer, to give the patients back a part of their identity ( 7 ). Outside the context of medical applications, GAN can also be used as classifiers to detect and classify the subject's emotional response ( 8 ). It can be beneficial for a plethora of applications, including patient health monitoring, crowd behavior tracking, predicting demographics ( 9 ) and similar behavioral applications.…”

Section: Using Deepfakes and Gans To Create Valuementioning

confidence: 99%

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

Neethirajan

2021

Front. Vet. Sci.

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Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.

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Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

Cited by 21 publications

References 35 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

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