Exploration of the Relation between Input Noise and Generated Image in Generative Adversarial Networks

In this paper,we propose a hybrid model aiming to map the input noise vector to the label of the generated image by the generative adversarial network(GAN).This model mainly consists of a pre-trained deep convolution generative adversarial network(DCGAN)and a classifier.By using the model,we visualize the distribution of two-dimensional input noise,leading to a specific type of the generated image after each training epoch of GAN.The visualization reveals the distribution feature of the input noise vector and the performance of the generator.With this feature,we try to build a guided generator(GG)with the ability to produce a fake image we need.Two methods are proposed to build GG.One is the most significant noise(MSN)method,and the other utilizes labeled noise.The MSN method can generate images precisely but with less variations.In contrast,the labeled noise method has more variations but is slightly less stable.Finally,we propose a criterion to measure the performance of the generator,which can be used as a loss function to effectively train the network.

Generative Adversarial Networks:Introduction and Outlook

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea.The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution.Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application fields.Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

GAN-GLS:Generative Lyric Steganography Based on Generative Adversarial Networks

Steganography based on generative adversarial networks(GANs)has become a hot topic among researchers.Due to GANs being unsuitable for text fields with discrete characteristics,researchers have proposed GANbased steganography methods that are less dependent on text.In this paper,we propose a new method of generative lyrics steganography based on GANs,called GAN-GLS.The proposed method uses the GAN model and the largescale lyrics corpus to construct and train a lyrics generator.In this method,the GAN uses a previously generated line of a lyric as the input sentence in order to generate the next line of the lyric.Using a strategy based on the penalty mechanism in training,the GAN model generates non-repetitive and diverse lyrics.The secret information is then processed according to the data characteristics of the generated lyrics in order to hide information.Unlike other text generation-based linguistic steganographic methods,our method changes the way that multiple generated candidate items are selected as the candidate groups in order to encode the conditional probability distribution.The experimental results demonstrate that our method can generate highquality lyrics as stego-texts.Moreover,compared with other similar methods,the proposed method achieves good performance in terms of imperceptibility,embedding rate,effectiveness,extraction success rate and security.

General image classification method based on semi-supervised generative adversarial networks

Generative adversarial networks(GANs) have become a competitive method among computer vision tasks. There have been many studies devoted to utilizing generative network to do generative tasks, such as images synthesis. In this paper, a semi-supervised learning scheme is incorporated with generative adversarial network on image classification tasks to improve the image classification accuracy. Two applications of GANs are mainly focused on: semi-supervised learning and generation of images which can be as real as possible. The whole process is divided into two sections. First, only a small part of the dataset is utilized as labeled training data. And then a huge amount of samples generated from the generator is added into the training samples to improve the generalization of the discriminator. Through the semi-supervised learning scheme, full use of the unlabeled data is made which may contain potential information. Thus, the classification accuracy of the discriminator can be improved. Experimental results demonstrate the improvement of the classification accuracy of discriminator among different datasets, such as MNIST, CIFAR-10.

Ballistic response of armour plates using Generative Adversarial Networks

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V50ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity(BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network(GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V50BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.

Evolution and Effectiveness of Loss Functions in Generative Adversarial Networks

Recently,the evolution of Generative Adversarial Networks(GANs)has embarked on a journey of revolutionizing the field of artificial and computational intelligence.To improve the generating ability of GANs,various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples,and the effectiveness of the loss functions in improving the generating ability of GANs.In this paper,we present a detailed survey for the loss functions used in GANs,and provide a critical analysis on the pros and cons of these loss functions.First,the basic theory of GANs along with the training mechanism are introduced.Then,the most commonly used loss functions in GANs are introduced and analyzed.Third,the experimental analyses and comparison of these loss functions are presented in different GAN architectures.Finally,several suggestions on choosing suitable loss functions for image synthesis tasks are given.

基于改进CGANs的入侵检测方法研究

近年来,机器学习算法在入侵检测系统(IDS)中的应用获得越来越多的关注。然而,传统的机器学习算法更多的依赖于已知样本,因此需要尽可能多的数据样本来对模型进行训练。遗憾地是,随着越来越多未知攻击的出现,且用于训练的攻击样本具有不平衡性,传统的机器学习模型会遇到瓶颈。文章提出一种将改进后的条件生成对抗网络(CGANs)与深度神经网络(DNN)相结合的入侵检测模型(CGANs-DNN),通过解决样本不平衡性问题来提高检测模型对未知攻击类型或只有少数攻击样本类型的检测率。深度神经网络(DNN)具有表征数据潜在特征的能力,而经过改进后的条件CGANs,能够通过学习已知攻击样本潜在数据特征分布,来根据指定类型生成新的攻击样本。此外,与生成对抗网络(GANs)和变分自编码器(VAE)等无监督生成模型相比,CGANsDNN经过改进后加入梯度惩罚项,在训练的稳定性上有了很大地提升。通过NSL-KDD数据集对模型进行评估,与传统算法相比CGANs-DNN不仅在整体准确率、召回率和误报率等方面有更好的性能,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。

基于生成对抗网络(GANs)革新包装设计

深度学习模型生成对抗网络(GANs)可以在包装设计领域良好的应用,探讨如何使用生成对抗网络(GANs)来革新包装设计。阐述GANs的基本原理和工作方式,引入了一种基于GANs的新型包装设计方法,通过将GANs应用于包装设计的生成和选择中,可以实现更加创新、个性化的包装设计效果。通过GANs在包装设计中的应用,可以大幅提高设计的多样性、创意性以及用户体验。

非均衡小样本条件下基于SAE - ACGANs的复杂 供输机构故障诊断方法

非均衡和小样本问题是制约深度学习技术在复杂供输机构故障诊断领域中应用效果的关键因素。为克服传统深度学习方法难以获取小样本数据内在分布和传统非均衡数据处理方法未考虑类别信息均衡化的缺陷,提出一种基于稀疏自编码辅助分类生成式对抗网络(sparse autoencoder auxiliary classifier generative adversarial networks,SAE-ACGANs)的复杂供输机构故障诊断方法。首先,对供输机构振动信号进行连续小波变换(continuous wavelet transform,CWT),得到反映信号时频特征的二维时频图;然后,运用稀疏编码器提取图像特征,并与类别信息融合为隐变量,强化其表征与图像所属类别相关特征的能力;生成器将融合隐变量映射为与真实样本分布类似的生成样本,从而扩展训练数据集;判别器从扩展数据集中挖掘有效的深度特征,并实现样本真伪和类别的判断;最后,通过优化后的生成器和判别器对抗学习训练机制,相互交替优化以达到纳什平衡,提高方法在非均衡小样本条件下样本生成质量和故障判定能力。复杂供输机构台架试验研究结果表明:SAE-ACGANs框架可以充分学习输入样本的内在分布和深度特征,相较于原ACGANs框架提升了判别器的性能,实现了模型收敛速度、训练精度和稳定性的提升;相较于传统非均衡数据处理算法,模型有效改善多数类分类偏好的影响,对于少数类故障样本的识别能力大幅提升。

Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.

Single Image Dehazing: An Analysis on Generative Adversarial Network

Haze is a very common phenomenon that degrades or reduces visibility. It causes various problems where high-quality images are required such as traffic and security monitoring. So haze removal from scenes is an immediate demand for clear vision. Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance. But it is unclear how these algorithms would perform on hazy images acquired “in the wild” and how we could gauge the progress in the field. To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN. We have experimented using benchmark dataset consisting of both synthetic and real-world hazy images. The obtained results are evaluated both quantitatively and qualitatively. Among these techniques, the DHSGAN gives the best performance.

Solar image deconvolution by generative adversarial network

With aperture synthesis(AS)technique,a number of small antennas can be assembled to form a large telescope whose spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna.In contrast from a direct imaging system,an AS telescope captures the Fourier coefficients of a spatial object,and then implement inverse Fourier transform to reconstruct the spatial image.Due to the limited number of antennas,the Fourier coefficients are extremely sparse in practice,resulting in a very blurry image.To remove/reduce blur,“CLEAN”deconvolution has been widely used in the literature.However,it was initially designed for a point source.For an extended source,like the Sun,its efficiency is unsatisfactory.In this study,a deep neural network,referring to Generative Adversarial Network(GAN),is proposed for solar image deconvolution.The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images.The main purpose of this work is visual inspection instead of quantitative scientific computation.We believe that this will also help scientists to better understand solar phenomena with high quality images.

Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for temporal coherence across frames.In this paper,we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network(DD-GAN).The DDGAN comprises a Deep Deconvolutional Neural Network(DDNN)as a Generator(G)and a modified Deep Convolutional Neural Network(DCNN)as a Discriminator(D)to ensure temporal coherence between adjacent frames.The proposed research involves several steps.First,the input text is fed into a Long Short Term Memory(LSTM)based text encoder and then smoothed using Conditioning Augmentation(CA)techniques to enhance the effectiveness of the Generator(G).Next,using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator(D),effectively distinguishing between generated and real videos.This research evaluates the quality of the generated videos using standard metrics like Inception Score(IS),Fréchet Inception Distance(FID),Fréchet Inception Distance for video(FID2vid),and Generative Adversarial Metric(GAM),along with a human study based on realism,coherence,and relevance.By conducting experiments on Single-Digit Bouncing MNIST GIFs(SBMG),Two-Digit Bouncing MNIST GIFs(TBMG),and a custom dataset of essential mathematics videos with related text,this research demonstrates significant improvements in both metrics and human study results,confirming the effectiveness of DD-GAN.This research also took the exciting challenge of generating preschool math videos from text,handling complex structures,digits,and symbols,and achieving successful results.The proposed research demonstrates promising results for generating coherent videos from textual input.

Generative Adversarial Network with Separate Learning Rule for Image Generation

Boundary equilibrium generative adversarial networks(BEGANs)are the improved version of generative adversarial networks(GANs).In this paper,an improved BEGAN with a skip-connection technique in the generator and the discriminator is proposed.Moreover,an alternative time-scale update rule is adopted to balance the learning rate of the generator and the discriminator.Finally,the performance of the proposed method is quantitatively evaluated by Fréchet inception distance(FID)and inception score(IS).The test results show that the performance of the proposed method is better than that of the original BEGAN.

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

基于生成对抗Transformer的电力负荷数据异常检测

电力负荷异常数据将给电力系统规划、负荷预测以及用能分析等带来较大的负面影响,因此亟须对负荷数据异常进行检测与识别。首先,针对电力负荷数据异常分类、原因及其特征开展分析。其次,改进传统Transformer编码器结构,采用多头注意力层代替掩码多头注意力层,同时移除前馈网络,以提高模型对负荷时序序列的全局注意力。基于生成对抗网络(generative adversarial networks,GAN)生成器与判别器的博弈结构,提出一种改进的GAN-Transformer模型,以更好地捕捉趋势性特征并加速模型收敛。然后,引入多阶段映射与训练方法,综合焦点分数打分机制,通过分阶段负荷序列重构帮助模型更好地提取负荷数据异常特征。最后,算例分析结果表明,GAN-Transformer模型在负荷数据异常检测精确率、召回率、F_(1)值以及训练时间方面均具有更优的性能,验证了所提方法的有效性和优越性。文中研究工作为基于深度学习进一步实现电力负荷数据异常分类与数据修复提供了有益参考。

Emotional dialog generation via multiple classifiers based on a generative adversarial network

Background Human-machine dialog generation is an essential topic of research in the field of natural language processing.Generating high-quality,diverse,fluent,and emotional conversation is a challenging task.Based on continuing advancements in artificial intelligence and deep learning,new methods have come to the forefront in recent times.In particular,the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses.However,neural network models come with their own set of questions and challenges.The basic conversational model framework tends to produce universal,meaningless,and relatively"safe"answers.Methods Based on generative adversarial networks(GANs),a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation.The proposed model comprises a generative and three discriminative models.The generator is based on the basic sequence-to-sequence(Seq2Seq)dialog generation model,and the aggregate discriminative model for the overall framework consists of a basic discriminative model,an emotion discriminative model,and a fluency discriminative model.The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus.The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion,and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion.Finally,the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences.Results Based on the experimental results,this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy,fluency,and consistency.Conclusions The proposed EMC-GAN model is capable of generating consistent,smooth,and fluent dialog that conveys pre-specified emotions,and exhibits better performance with respect to emotional accuracy,consistency,and fluency compared to its competitors.

基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法

针对低信噪比条件下的扩频与常规调制信号分类精度低的问题,该文提出一种基于生成式对抗网络(GAN)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多模态注意力机制信号调制识别方法。首先生成待识别信号的时频图像(TFIs),并利用GAN实现TFIs降噪处理;然后将信号的同相正交数据(I/Q data)与TFIs作为模型输入,并搭建基于CNN的TFIs识别支路和基于LSTM的I/Q数据识别支路;最后,在模型中添加注意力机制,增强I/Q数据和TFIs中重要特征对分类结果的决定作用。实验结果表明,该文所提方法相较于单模态识别模型以及其它基线模型,整体分类精度有效提升2%~7%,并在低信噪比条件下具备更强的特征表达能力和鲁棒性。

基于BERT与生成对抗的民航陆空通话意图挖掘

针对民航陆空通话领域语料难以获取、实体分布不均,以及意图信息提取中实体规范不足且准确率有待提升等问题,为了更好地提取陆空通话意图信息,提出一种融合本体的基于双向转换编码器(bidirectional encoder representations from transformers,BERT)与生成对抗网络(generative adversarial network,GAN)的陆空通话意图信息挖掘方法,并引入航班池信息对提取的部分信息进行校验修正,形成空中交通管制(air traffic control,ATC)系统可理解的结构化信息。首先,使用改进的GAN模型进行陆空通话智能文本生成,可有效进行数据增强,平衡各类实体信息分布并扩充数据集;然后,根据欧洲单一天空空中交通管理项目定义的本体规则进行意图的分类与标注;之后,通过BERT预训练模型生成字向量并解决一词多义问题,利用双向长短时记忆(bidirectional long short-term memory,BiLSTM)网络双向编码提取上下句语义特征,同时将该语义特征送入条件随机场(conditional random field,CRF)模型进行推理预测,学习标签的依赖关系并加以约束,以获取全局最优结果;最后,根据编辑距离(edit distance,ED)算法进行意图信息合理性校验与修正。对比实验结果表明,所提方法的宏平均F_(1)值达到了98.75%,在民航陆空通话数据集上的意图挖掘性能优于其他主流模型,为其加入数字化进程奠定了基础。