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

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.

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

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.

scholarly journals Low-Light Image Enhancement Based on Generative Adversarial Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent P. M.
Keyword(s):  
Image Enhancement ◽  
Visual Information ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Light Image ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Dim Light ◽  
Effective Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

scholarly journals True Orthoimage Generation Using Airborne LiDAR Data with Generative Adversarial Network-Based Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Young Ha Shin ◽  
Dong-Cheon Lee
Keyword(s):  
Deep Learning ◽  
Laser Scanner ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Generative Adversarial Network ◽  
Cloud Data ◽  
Adversarial Network ◽  
Benchmark Datasets

Orthoimage, which is geometrically equivalent to a map, is one of the important geospatial products. Displacement and occlusion in optical images are caused by perspective projection, camera tilt, and object relief. A digital surface model (DSM) is essential data for generating true orthoimages to correct displacement and to recover occlusion areas. Light detection and ranging (LiDAR) data collected from an airborne laser scanner (ALS) system is a major source of DSM. The traditional methods require sophisticated procedures to produce a true orthoimage. Most methods utilize 3D coordinates of the DSM and multiview images with overlapping areas for orthorectifying displacement and detecting and recovering occlusion areas. LiDAR point cloud data provides not only 3D coordinates but also intensity information reflected from object surfaces in the georeferenced orthoprojected space. This paper proposes true orthoimage generation based on a generative adversarial network (GAN) deep learning (DL) with the Pix2Pix model using intensity and DSM of the LiDAR data. The major advantage of using LiDAR data is that the data is occlusion-free true orthoimage in terms of projection geometry except in the case of low image quality. Intensive experiments were performed using the benchmark datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The results demonstrate that the proposed approach could have the capability of efficiently generating true orthoimages directly from LiDAR data. However, it is crucial to find appropriate preprocessing to improve the quality of the intensity of the LiDAR data to produce a higher quality of the true orthoimages.

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

Optik ◽  
2021 ◽  
Vol 227 ◽  
pp. 166060
Author(s):  
Yangdi Hu ◽  
Zhengdong Cheng ◽  
Xiaochun Fan ◽  
Zhenyu Liang ◽  
Xiang Zhai
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Single Pixel

scholarly journals Machine Learning for Dissimulating Reality

Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 17
Author(s):  
Andrea Giussani
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Huge Amount ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Technological Advances ◽  
Textual Data ◽  
Musical Scores ◽  
Mathematical Formulas

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.

SGAN4AbSum: A Semantic-Enhanced Generative Adversarial Network for Abstractive Text Summarization

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Ground Truth ◽  
Text Summarization ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Deep Recurrent Neural Network ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Abstractive Summarization

Abstract In abstractive summarization task, most of proposed models adopt the deep recurrent neural network (RNN)-based encoder-decoder architecture to learn and generate meaningful summary for a given input document. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. Moreover, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also make the generated summaries unnatural and incoherent. To deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model in which train the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network (GCN) textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

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.

scholarly journals RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

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

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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