Optimizing the quality of Fourier single-pixel imaging via generative adversarial network

In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being. Modern technological advances such as OpenAI’s GPT-2 (and recently GPT-3) permit automated systems to dramatically alter reality with synthetic outputs so that humans are not able to distinguish the real copy from its counteracts. An example is given by an article entirely written by GPT-2, but many other examples exist. In the field of computer vision, Nvidia’s Generative Adversarial Network, commonly known as StyleGAN (Karras et al. 2018), has become the de facto reference point for the production of a huge amount of fake human face portraits; additionally, recent algorithms were developed to create both musical scores and mathematical formulas. This presentation aims to stimulate participants on the state-of-the-art results in this field: we will cover both GANs and language modeling with recent applications. The novelty here is that we apply a transformer-based machine learning technique, namely RoBerta (Liu et al. 2019), to the detection of human-produced versus machine-produced text concerning fake news detection. RoBerta is a recent algorithm that is based on the well-known Bidirectional Encoder Representations from Transformers algorithm, known as BERT (Devlin et al. 2018); this is a bi-directional transformer used for natural language processing developed by Google and pre-trained over a huge amount of unlabeled textual data to learn embeddings. We will then use these representations as an input of our classifier to detect real vs. machine-produced text. The application is demonstrated in the presentation.

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Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

Journal of Integrated Design and Process Science ◽

10.3233/jid-210019 ◽

2021 ◽

pp. 1-17

Author(s):

Khaled ELKarazle ◽

Valliappan Raman ◽

Patrick Then

Keyword(s):

Age Estimation ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Original Dataset ◽

Age Group Classification ◽

Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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Improvement of Image Quality of Cone-beam CT Images by Three-dimensional Generative Adversarial Network

10.1109/embc46164.2021.9629952 ◽

2021 ◽

Author(s):

Takumi Hase ◽

Megumi Nakao ◽

Keiho Imanishi ◽

Mitsuhiro Nakamura ◽

Tetsuya Matsuda

Keyword(s):

Image Quality ◽

Cone Beam Ct ◽

Three Dimensional ◽

Ct Images ◽

Cone Beam ◽

Generative Adversarial Network ◽

Adversarial Network

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InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

Remote Sensing ◽

10.3390/rs13193971 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3971

Author(s):

Wenxiang Chen ◽

Yingna Li ◽

Zhengang Zhao

Keyword(s):

Transmission Line ◽

Transmission Lines ◽

State Of The Art ◽

Generative Adversarial Network ◽

Detection Model ◽

Adversarial Network ◽

Monte Carlo Search ◽

Model Training ◽

Inspection Tasks

Insulator detection is one of the most significant issues in high-voltage transmission line inspection using unmanned aerial vehicles (UAVs) and has attracted attention from researchers all over the world. The state-of-the-art models in object detection perform well in insulator detection, but the precision is limited by the scale of the dataset and parameters. Recently, the Generative Adversarial Network (GAN) was found to offer excellent image generation. Therefore, we propose a novel model called InsulatorGAN based on using conditional GANs to detect insulators in transmission lines. However, due to the fixed categories in datasets such as ImageNet and Pascal VOC, the generated insulator images are of a low resolution and are not sufficiently realistic. To solve these problems, we established an insulator dataset called InsuGenSet for model training. InsulatorGAN can generate high-resolution, realistic-looking insulator-detection images that can be used for data expansion. Moreover, InsulatorGAN can be easily adapted to other power equipment inspection tasks and scenarios using one generator and multiple discriminators. To give the generated images richer details, we also introduced a penalty mechanism based on a Monte Carlo search in InsulatorGAN. In addition, we proposed a multi-scale discriminator structure based on a multi-task learning mechanism to improve the quality of the generated images. Finally, experiments on the InsuGenSet and CPLID datasets demonstrated that our model outperforms existing state-of-the-art models by advancing both the resolution and quality of the generated images as well as the position of the detection box in the images.

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Complete Face Recovering: An Approach towards Recognizing a Person by a Single Partial Face Image without the Target Photo in Gallery

10.36227/techrxiv.12333176.v1 ◽

2020 ◽

Author(s):

Yiu-ming Cheung ◽

Mengke Li

Keyword(s):

Promising Result ◽

Face Image ◽

Attractive Potential ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Discriminative Feature ◽

New Variant ◽

Potential Applications ◽

Benchmark Datasets

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

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Complete Face Recovering: An Approach towards Recognizing a Person by a Single Partial Face Image without the Target Photo in Gallery

10.36227/techrxiv.12333176 ◽

2020 ◽

Author(s):

Yiu-ming Cheung ◽

Mengke Li

Keyword(s):

Promising Result ◽

Face Image ◽

Attractive Potential ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Discriminative Feature ◽

New Variant ◽

Potential Applications ◽

Benchmark Datasets

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

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Discovering Novel Antimicrobial Peptides in Generative Adversarial Network

10.1101/2021.11.22.469634 ◽

2021 ◽

Author(s):

Tzu-Tang Lin ◽

Li-Yen Yang ◽

Ching-Tien Wang ◽

Ga-Wen Lai ◽

Chi-Fong Ko ◽

...

Keyword(s):

Antimicrobial Peptides ◽

Antibacterial Activities ◽

Resistant Bacteria ◽

Antibacterial Effects ◽

Generative Adversarial Network ◽

Antibiotic Resistant ◽

Adversarial Network ◽

Carbapenem Resistant ◽

Disk Diffusion Testing

Due to the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) can become ideal for next-generation antibiotics. This study trained a deep convolutional generative adversarial network (GAN) with known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them named GAN-pep 1~8 were chosen to be synthesized for further experiments. Disk diffusion testing and minimum inhibitory concentration (MIC) determination were used to determine the antibacterial effects of the synthesized GAN-designed peptides. Seven out of the eight synthesized GAN-designed peptides showed antibacterial activities. Additionally, GAN-pep 3 and GAN-pep 8 had a broad spectrum of antibacterial effects. Both of them were also effective against antibiotic-resistant bacteria strains such as methicillin-resistant Staphylococcus aureus (S. aureus) and carbapenem-resistant Pseudomonas aeruginosa (P. aeruginosa). GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria.

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Coverless Image Steganography Based on Generative Adversarial Network

Mathematics ◽

10.3390/math8091394 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1394 ◽

Cited By ~ 2

Author(s):

Jiaohua Qin ◽

Jing Wang ◽

Yun Tan ◽

Huajun Huang ◽

Xuyu Xiang ◽

...

Keyword(s):

Deep Learning ◽

Poor Quality ◽

Image Steganography ◽

Cover Image ◽

Secret Message ◽

Generative Adversarial Network ◽

Learning Framework ◽

Adversarial Network ◽

Traditional Image

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

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AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Sensors ◽

10.3390/s19245479 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5479 ◽

Cited By ~ 1

Author(s):

Maryam Rahnemoonfar ◽

Jimmy Johnson ◽

John Paden

Keyword(s):

Data Augmentation ◽

Synthetic Data ◽

Detection Algorithm ◽

Contour Detection ◽

Training Data ◽

Generative Adversarial Network ◽

Radar Images ◽

Adversarial Network ◽

Radar Imagery

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

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Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning

Applied Sciences ◽

10.3390/app10134528 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4528

Author(s):

Je-Yeol Lee ◽

Sang-Il Choi

Keyword(s):

Generative Models ◽

Learning Processes ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Secondary Network ◽

Variational Autoencoder ◽

Variational Learning

In this paper, we propose a new network model using variational learning to improve the learning stability of generative adversarial networks (GAN). The proposed method can be easily applied to improve the learning stability of GAN-based models that were developed for various purposes, given that the variational autoencoder (VAE) is used as a secondary network while the basic GAN structure is maintained. When the gradient of the generator vanishes in the learning process of GAN, the proposed method receives gradient information from the decoder of the VAE that maintains gradient stably, so that the learning processes of the generator and discriminator are not halted. The experimental results of the MNIST and the CelebA datasets verify that the proposed method improves the learning stability of the networks by overcoming the vanishing gradient problem of the generator, and maintains the excellent data quality of the conventional GAN-based generative models.

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