scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11 ◽  
Author(s):  
Ariyo Oluwasanmi ◽  
Muhammad Umar Aftab ◽  
Zhiguang Qin ◽  
Son Tung Ngo ◽  
Thang Van Doan ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Extended Period ◽  
Real Data ◽  
Ct Scans ◽  
Diagnostic Process ◽  
Automatic Identification ◽  
Rt Pcr ◽  
Generative Adversarial Network ◽  
Adversarial Network

The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.

scholarly journals Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews

2021 ◽  
Author(s):  
Cristóbal Colón-Ruiz
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Automated Analysis ◽  
Learning Approaches ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Wide Range ◽  
Online User Reviews

<div>Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.</div><div>These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. </div><div>In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.</div><div>To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. </div><div>Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.</div>

scholarly journals Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews

2021 ◽  
Author(s):  
Cristóbal Colón-Ruiz
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Automated Analysis ◽  
Learning Approaches ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Wide Range ◽  
Online User Reviews

<div>Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.</div><div>These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. </div><div>In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.</div><div>To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. </div><div>Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.</div>

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals Generating Natural Counterfactual Visual Explanations

2020 ◽  
Author(s):  
Wenqi Zhao ◽  
Satoshi Oyama ◽  
Masahito Kurihara
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Classification Task ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Class A ◽  
Adversarial Network ◽  
Class B ◽  
Machine Learning Models

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.

scholarly journals JANE: Jointly Adversarial Network Embedding

2020 ◽  
Author(s):  
Liang Yang ◽  
Yuexue Wang ◽  
Junhua Gu ◽  
Chuan Wang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Link Prediction ◽  
Real Data ◽  
Semantic Space ◽  
Network Embedding ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Adversarial Network ◽  
Node Clustering ◽  
Topology Information ◽  
Embedding Methods

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).

Sound Field Reconstruction in Rooms with Deep Generative Models

2021 ◽  
Vol 263 (5) ◽  
pp. 1527-1538
Author(s):  
Xenofon Karakonstantis ◽  
Efren Fernandez Grande
Keyword(s):  
Plane Waves ◽  
Sound Field ◽  
Real Data ◽  
Generative Models ◽  
Random Wave ◽  
Generative Adversarial Network ◽  
Underlying Distribution ◽  
Adversarial Network ◽  
Reconstruction Methods ◽  
Free Region

The characterization of Room Impulse Responses (RIR) over an extended region in a room by means of measurements requires dense spatial with many microphones. This can often become intractable and time consuming in practice. Well established reconstruction methods such as plane wave regression show that the sound field in a room can be reconstructed from sparsely distributed measurements. However, these reconstructions usually rely on assuming physical sparsity (i.e. few waves compose the sound field) or trait in the measured sound field, making the models less generalizable and problem specific. In this paper we introduce a method to reconstruct a sound field in an enclosure with the use of a Generative Adversarial Network (GAN), which s new variants of the data distributions that it is trained upon. The goal of the proposed GAN model is to estimate the underlying distribution of plane waves in any source free region, and map these distributions from a stochastic, latent representation. A GAN is trained on a large number of synthesized sound fields represented by a random wave field and then tested on both simulated and real data sets, of lightly damped and reverberant rooms.

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