SSC-GAN: A Novel GAN Based on the Same Solution Constraints of First-Order ODEs

2021 ◽  
pp. 2152018
Author(s):  
Hongyou Chen ◽  
Fan Chen ◽  
Hongjie He
Keyword(s):  
Synthesis Method ◽  
Optimal Solution ◽  
Image Synthesis ◽  
Stability And Convergence ◽  
Natural Image ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
First Order ◽  
Adversarial Network ◽  
Better Than

Generative adversarial network (GAN) is the most important natural image synthesis method, and one of GAN major challenges is the adversarial learning stability. Usually, GAN directly optimizes a single divergence via the two-player zero-sum game of generator and discriminator. Because the parameter updating of generator and discriminator is complex and changeable, directly optimizing the single divergence is easy to fall into local optimal solution, which affects the adversarial learning stability. To improve the adversarial learning stability and convergence accuracy, the same solution constraints GAN (SSC-GAN) is proposed. In this novel GAN, a first-order ordinary differential equation (ODE) is constructed by training set probability density function, which is the same solution problem of GAN optimization problem, and its constraint conditions are given to ensure the existence and uniqueness of the ODE solution. These constraints are used to guide the GAN parameter updating to improve the adversarial learning stability, thereby getting better training effects. In CELEBA and CIFAR10, the experimental results show that the novel GAN is better than the classic GAN models. In CELEBA, CIFAR10 and LSUN-BEDROOM, the proposed SSC-GAN is also better than two state-of-the-art GAN models, SAGAN and SNGAN.

scholarly journals Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 734 ◽  
Author(s):  
Yan Ma ◽  
Kang Liu ◽  
Zhibin Guan ◽  
Xinkai Xu ◽  
Xu Qian ◽  
...  
Keyword(s):  
Object Recognition ◽  
Synthesis Method ◽  
Interactive Systems ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Augmented Reality (AR) is crucial for immersive Human–Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry . Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

scholarly journals Generative adversarial network image synthesis method for skin lesion generation and classification

2021 ◽  
Vol 11 (4) ◽  
pp. 237
Author(s):  
Saeed Meshgini ◽  
Freedom Mutepfe ◽  
BehnamKiani Kalejahi ◽  
Sebelan Danishvar
Keyword(s):  
Skin Lesion ◽  
Synthesis Method ◽  
Image Synthesis ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhangguo Tang ◽  
Junfeng Wang ◽  
Huanzhou Li ◽  
Jian Zhang ◽  
Junhao Wang
Keyword(s):  
Loss Function ◽  
Learning Algorithm ◽  
Synthesis Method ◽  
Covert Channel ◽  
Covert Communication ◽  
New Paradigm ◽  
Generative Adversarial Network ◽  
Good Convergence ◽  
Adversarial Network ◽  
Activity Loss

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of “cognitive deception” is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of “functional equivalence” and “cognitive equivalence” and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication.

scholarly journals IntroVNMT: An Introspective Model for Variational Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8830-8837
Author(s):  
Xin Sheng ◽  
Linli Xu ◽  
Junliang Guo ◽  
Jingchang Liu ◽  
Ruoyu Zhao ◽  
...  
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Image Synthesis ◽  
Target Language ◽  
Generative Adversarial Network ◽  
Neural Machine Translation ◽  
Adversarial Network ◽  
Proposed Model ◽  
Model Training ◽  
High Level

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.

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).

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