Underwater images play an essential role in acquiring and understanding underwater information. High-quality underwater images can guarantee the reliability of underwater intelligent systems. ...Unfortunately, underwater images are characterized by low contrast, color casts, blurring, low light, and uneven illumination, which severely affects the perception and processing of underwater information. To improve the quality of acquired underwater images, numerous methods have been proposed, particularly with the emergence of deep learning technologies. However, the performance of underwater image enhancement methods is still unsatisfactory due to lacking sufficient training data and effective network structures. In this paper, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear underwater image is achieved by a multi-scale generator. Besides, we employ a dual discriminator to grab local and global semantic information, which enforces the generated results by the multi-scale generator realistic and natural. Experiments on real-world and synthetic underwater images demonstrate that the proposed method performs favorable against the state-of-the-art underwater image enhancement methods.
The methods for remote sensing image (RSI) scene classification based on deep convolutional neural networks (DCNNs) have achieved prominent success. However, confronted with adversarial examples ...obtained by adding imperceptible perturbations to clean images, the great vulnerability of DCNNs makes it worth exploring effective defense methods. To date, numerous countermeasures for adversarial examples have been proposed, but how to improve the defensive ability for unknown attacks still to be answered. To address this issue, in this article, we propose an effective defense framework specified for RSI scene classification, named perturbation-seeking generative adversarial networks (PSGANs). In brief, a new training framework is designed to train the classifier by introducing the examples generated during the image reconstruction process, in addition to clean examples and adversarial ones. These generated examples can be random kinds of unknown attacks during training and thus are utilized to eliminate the blind spots of a classifier. To assist the proposed training framework, a reconstruction method is developed. First, instead of modeling the distribution of clean examples, we model the distributions of the perturbations added in adversarial examples. Second, to make a tradeoff between the diversity of the reconstructed examples and the optimization of PSGAN, a scale factor named seeking radius is introduced to scale the generated perturbations before they are subtracted by the given adversarial examples. Comprehensive and extensive experimental results on three widely used benchmarks for RSI scene classification demonstrate the great effectiveness of PSGAN when faced with both known and unknown attacks. Our source code is available at https://github.com/xuxiangsun/PSGAN .
Low fault diagnosis accuracy in case of insufficient and imbalanced samples is a major problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large difference in the ...number of samples of different categories or the lack of a certain fault sample, which requires good learning of the characteristics of a small number of samples. Sample generation in the deep learning generation model can effectively solve this problem. In this study, we proposed a novel multiclass wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multisource signals fusion. This strategy converts multisource 1-D vibration signals into 2-D signals, and the multisource 2-D signals were fused by using wavelet transform. The CVAE-GAN model was developed by merging the variational autoencoder (VAE) with the generative adversarial network (GAN). The VAE encoder was introduced as the front end of the GAN generator. The sample label was introduced as the model input to improve the model's training efficiency. Finally, the sample set was used to train encoder, generator, and discriminator in the CVAE-GAN model to supplement the number of the fault samples. In the classifier, the sample set is used to do experimental analysis under various sample circumstances. The results show that the proposed strategy can increase wind turbine bearing fault diagnostic accuracy in complex scenarios.
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for early stages of AD is of great ...clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, using the class discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss, and <inline-formula> <tex-math notation="LaTeX">{L}1 </tex-math></inline-formula> penalty, a single generator in MP-GAN can learn the class discriminative maps for multiple classes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.
Generative adversarial networks (GANs) are famous for generating samples by training a generator and a discriminator via an adversarial procedure. For hyperspectral image classification, the ...collection of samples is always difficult. However, directly applying GAN to hyperspectral image classification exists two problems. One is that the generated samples lack discriminative information. Meanwhile, the discriminator has no discriminative ability for multiclassification. Another is that spatial and spectral information requires to be considered in hyperspectral image classification simultaneously. To address these problems, a novel multiclass spatial-spectral GAN (MSGAN) method is proposed. In MSGAN, two generators are devised to generate the samples containing spatial and spectral information, respectively, and the discriminator is devised to extract joint spatial-spectral features and output multiclass probabilities. Moreover, novel adversarial objectives for multiclass are defined. The discriminator is devised to predict training samples belonging to true classes and generated samples belonging to all the classes with the same probability. The generators are devised to make the discriminator mistake. By adversarial learning between the discriminator and generators, the classification performance of the discriminator is promoted with the assistance of discriminative generated samples. Experimental results on hyperspectral images demonstrate that the proposed method achieves encouraging classification performance compared with several state-of-the-art methods, especially with the limited training samples.
Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, in this article, a novel federated deep ...generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.
Intelligent fault diagnosis of machines has long been a research hotspot and has achieved fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small sample due ...to the lack of fault signals. To solve this problem, a small sample focused intelligent fault diagnosis method via multimodules gradient penalized generative adversarial networks is proposed. The proposed method consists of three network modules: generator, discriminator, and classifier. By adversarial training, the generator can generate mechanical signals in different health conditions. Because of the high similarity to the signals obtained in practice, the generated signals can also be used as training data so that the limited training dataset of the proposed method is expanded. The classifier has a strong ability to extract fault features from raw mechanical signals and then classify different health conditions. The experimental results on two bearing vibration datasets indicate that the proposed method can not only generate bearing vibration signals but also obtain fairly high fault classificati on accuracy under the small sample condition.
Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural ...photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.
Abstract
In recent years, generative adversarial networks have performed well in the field of dialogue generation to improve the information diversity of dialogue responses. Often overlooked, ...however, is that the query and response are not relevant on the topic. In order to improve the topic relevance of chat conversation, the paper proposed a topic-relevance adversarial response generation model, TR-ARG, which is composed of generator G, discriminator D and topic classifier T. The experiment was evaluated on OpenSubtitles, an open dialog dataset, and compared with the current baseline models SEQ2SEQ and GAN-AEL. The results show that our model can effectively improve the topic relevance of generated responses.
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.
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