Learning discriminative and meaningful samples for generalized zero shot classification

Generalized zero shot classification aims to recognize both seen and unseen samples in test sets, which has gained great attention. Recently, many works consider using generative adversarial network to generate unseen samples for solving generalized zero shot classification problem. In this paper, we study how to generate discriminative and meaningful samples. We propose a method to learn discriminative and meaningful samples for generalized zero shot classification tasks (LDMS) by generative adversarial network with the regularization of class consistency and semantic consistency. In order to make the generated samples discriminative, class consistency is used, such that the generated samples of the same classes are near and of different classes are far away. In order to make the generated samples meaningful, semantic consistency is used, such that the semantic representations of the generated samples are close to their class prototypes. It encodes the discriminative information and semantic information to the generator. In order to alleviate the bias problem, we select some confident unseen samples. We use the seen samples, the generated unseen samples and the selected confident unseen samples to train the final classifier. Extensive experiments on all datasets demonstrate that the proposed method can outperform state-of-the-art models on generalized zero shot classification tasks.     

Semantic-aware visual attributes learning for zero-shot recognition

Zero-shot learning (ZSL) aims to recognize unseen image classes without requiring any training samples of these specific classes. The ZSL problem is typically achieved by building up a semantic embedding space like attributes to bridge the visual features and class labels of images. Currently, most ZSL approaches focus on learning a visual-semantic alignment from seen classes using only the human-designed attributes, and then ZSL problem is solved by transferring semantic knowledge from seen classes to the unseen classes. However, few works indicate if the human-designed attributes are discriminative enough for image class prediction. To address this issue, we propose a semantic-aware dictionary learning (SADL) framework to explore these discriminative visual attributes across seen and unseen classes. Furthermore, the semantic cues are elegantly integrated into the feature representations via learned visual attributes for recognition task. Experiments conducted on two challenging benchmark datasets show that our approach outweighs other state-of-the-art ZSL methods.     

LRGAN: Visual anomaly detection using GAN with locality-preferred recoding

Deep neural networks, including deep auto-encoder (DAE) and generative adversarial networks (GAN), have been extensively applied for visual anomaly detection. These models generally assume that reconstruction errors should be lower for normal samples but higher for anomalies. However, it has been found that DAE based models can sometimes reconstruct anomalies very well and thus result in false alarms or misdetections. To address this problem, we propose a model using GAN with locality-preferred recoding, named LRGAN. LRGAN is inspired by the observation that both normal and abnormal samples are not completely scattered throughout the latent space but clustered separately at some local regions. Therefore, a locality-preferred recoding (LR) module is designed to compulsively represent the latent vectors of anomalies by normal ones. As a result, reconstructions of anomalies will approximate to normal samples and corresponding residuals can thus be enlarged. To partly avoid latent vectors of normal samples being recoded, we further present an improved model using GAN with an adaptive LR (ALR) module, named LRGAN+. ALR applies the clustering algorithm to generate a more compact codebook; more importantly, it helps LRGAN + automatically skip the LR module for possible normal samples with a threshold strategy. Our proposed method is evaluated on two public datasets (i.e., MNIST and CIFAR-10) and one real-world industrial dataset (i.e., Fasteners), considering both one-class and multi-class anomaly detection protocols. Experimental results demonstrate that LRGAN is comparable with state-of-the-art methods and LRGAN + outperforms these methods on all datasets.     

Adaptive adversarial prototyping network for few-shot prototypical translation

Translating multiple real-world source images to a single prototypical image is a challenging problem. Notably, these source images belong to unseen categories that did not exist during model training. We address this problem by proposing an adaptive adversarial prototype network (AAPN) and enhancing existing one-shot classification techniques. To overcome the limitations that traditional works cannot extract samples from novel categories, our method tends to solve the image translation task of unseen categories through a meta-learner. We train the model in an adversarial learning manner and introduce a style encoder to guide the model with an initial target style. The encoded style latent code enhances the performance of the network with conditional target style images. The AAPN outperforms the state-of-the-art methods in one-shot classification of brand logo dataset and achieves the competitive accuracy in the traffic sign dataset. Additionally, our model improves the visual quality of the reconstructed prototypes in unseen categories. Based on the qualitative and quantitative analysis, the effectiveness of our model for few-shot classification and generation is demonstrated.     

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.     

GAN-based data augmentation of prohibited item X-ray images in security inspection

Convolutional neural networks (CNNs) based methods for automatic discriminant of prohibited items in X-ray images attract attention increasingly. However, it is difficult to train a reliable CNN model using the available X-ray security image databases, since they are not enough in sample quantity and diversity. Recently, generative adversarial network (GAN) has been widely used in image generation and regarded as a power model for data augmentation. In this paper, we propose a data augmentation method for X-ray prohibited item images based on GAN. First, the network structure and loss function of the self-attention generative adversarial network (SAGAN) are improved to generate the realistic X-ray prohibited item images. Then, the images generated by our model are evaluated using GAN-train and GAN-test. Experimental results of GAN-train and GAN-test are 99.91% and 98.82% respectively. It implies that our model can enlarge the X-ray prohibited item image database effectively.     

Fault detection and diagnosis method for heterogeneous wireless network based on GAN

Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.     

Coupled adversarial variational autoencoder

Generating image is a hot research topic in the field of deep learning, and it is challenging for generating high quality image pairs. The image pair refers to the corresponding image tuples with the same high-level features and different low-level features, generating high-quality image pairs has important applications in some specific fields. Currently, there are many methods to generate high quality images, but these methods cannot produce higher resolution image pairs. To address this problem, we proposed a novel model which consists of two adversarial variational autoencoders, each one aim at generating an image of pairs more accurately. We called this model CoAdVAE (coupled adversarial variational autoencoders), it can generate high quality image pairs due to introducing adversarial learning to the model. In the experiments, we applied the proposed model to three learning tasks, i.e., generating image pairs with different attributes, converting image attributes, and image dehazing. We show by experiments compared with related approaches on four datasets, Mnist, Celeba, AFHQ, and Fog_data that the proposed model can achieve the-state-of-the-art results.     

一种基于小波相位滤波及视觉非线性的医学图像自适应增强新方法

针对经典方法对ＳＮＲ低的医学图像存在噪声过度放大及伪像产生问题,本文在精细尺度上,根据信号与噪声的ＷＴ相位在相继尺度上关联性的不同进行去噪,在大尺度上则采用Ｓｅｍｉｓｏｆｔ阈法对ＤＷＴ系数进行快速缩减去噪,根据人眼的视觉特性对ＷＴ系数的增益进行非线性的自适应控制,较之经典方法,本文方法具有增强图像视觉效果佳,无伪像产生的优点,且在噪声抑制、保边沿及增强各种细节上效果良好。     

Learning object-centric complementary features for zero-shot learning

Zero-shot learning (ZSL) aims to recognize new objects that have never seen before by associating categories with their semantic knowledge. Existing works mainly focus on learning better visual-semantic mapping to align the visual and semantic space, while the effectiveness of learning discriminative visual features is neglected. In this paper, we propose an object-centric complementary features (OCF) learning model to take full advantage of visual information of objects with the guidance of semantic knowledge. This model can automatically discover the object region and obtain fine-scale samples without any human annotation. Then, the attention mechanism is used in our model to capture long-range visual features corresponding to semantic knowledge like ‘four legs’ and subtle visual differences between similar categories. Finally, we train our model with the guidance of semantic knowledge in an end-to-end manner. Our method is evaluated on three widely used ZSL datasets, CUB, AwA2, and FLO, and the experiment results demonstrate the efficacy of the object-centric complementary features, and our proposed method outperforms the state-of-the-art methods.     

小样本图像处理方法赋能的宽带频谱感知

针对强噪声环境下频谱感知方法计算复杂度高、难以获取大量标注样本、检测准确率低等问题,该文提出由图像去噪和图像分类思想驱动的频谱感知方法(IDCSS)。首先,对感知用户的接收信号进行时频变换,将无线电数值信号转换为图像。强噪声环境下感知用户接收信号图像与噪声图像相关度高,因此搭建生成对抗网络(GAN)来增加低信噪比下接收信号样本的数量,提高图像的质量。在生成器中,利用残差-长短时记忆网络取代生成网络U-Net结构中的跳跃连接,对图像进行去噪、提取感知用户接收信号图像的多尺度特征、建立基于熵的损失函数来构建网络的抗噪能力;在判决器中,设计适用无线电图像信号的多维度判决器来增强生成图像的质量、保留低信噪比感知用户信号的图像细节。最后利用分类器识别频谱占用状态。仿真结果表明,与现有频谱感知算法相比,所提算法具有较好的检测性能。     

一种Enlighten-GAN网络的指纹超分辨率重建方法北大核心CSCD

将低分辨率(LR)图像重建为高分辨率(HR)图像的主流模型是生成对抗网络(GAN)。然而,由于基于GAN的方法利用从其他图像中学习到的内容来恢复高频信息,在处理新的图像时往往会产生伪影。由于,指纹图像的特征比自然图像更加复杂。因此,将以前的网络应用于指纹图像,尤其是中等分辨率的图像,会导致收敛不稳定伪影效果更加严重。针对以上弊端,本文提出了一种Enlighten-GAN超分辨率方法,来解决指纹图像的重建问题。具体来说,我们设计了启发块来控制网络收敛到一个可靠的点,并利用自我监督分层感知损失以改进损失函数提升网络性能。实验结果证明Enlighten-GAN方法在指纹图像的重建效果性能上具有更加卓越的效果。     

基于生成对抗网络的差分隐私数据发布方法

机器学习的飞速发展使其成为数据挖掘领域最有效的工具之一,但算法的训练过程往往需要大量的用户数据,给用户带来了极大的隐私泄漏风险.由于数据统计特征的复杂性及语义丰富性,传统隐私数据发布方法往往需要对原始数据进行过度清洗,导致数据可用性低而难以再适用于数据挖掘任务.为此,提出了一种基于生成对抗网络（Generative Adversarial Network,GAN）的差分隐私数据发布方法,通过在GAN模型训练的梯度上添加精心设计的噪声来实现差分隐私,确保GAN可无限量生成符合源数据统计特性且不泄露隐私的合成数据.针对现有同类方法合成数据质量低、模型收敛缓慢等问题,设计多种优化策略来灵活调整隐私预算分配并减小总体噪声规模,同时从理论上证明了合成数据严格满足差分隐私特性.在公开数据集上与现有方法进行实验对比,结果表明本方法能够更高效地生成质量更高的隐私保护数据,适用于多种数据分析任务.     

基于无监督学习的单人多姿态图像生成方法

针对目前视觉监控领域中采集到的人物数据样本量少和特征单一的问题,提出了一种具有高视觉感知约束的双向生成对抗网络生成期望人物姿态图像的方法。采用给定人物的单个图像和期望姿态的二维骨架作为双向生成对抗网络的输入,生成具有该目标人物期望姿态的图像。将生成的期望姿态图像反映射回原始姿态图像,利用少量的图像以无监督学习方式进行学习,生成该人物期望姿态的高质量图像。提出的方法在DeepFashion公开数据集上进行了实验,结果表明,采用文中提出的方法生成的图像结构相似度(SSIM)比以往的方法提高了0.28,有效的提升了基于无监督学习的单人多姿态人物图像生成的质量。     

A novel deep neural network for noise removal from underwater image

Underwater image processing technologies have always been challenging tasks due to the complex underwater environment. Images captured under water are not only affected by the water itself, but also by the diverse suspended particles that increase the effect of absorption and scattering. Moreover, these particles themselves are usually imaged on the picture, causing the spot noise signal to interfere with the target objects. To address this issue, we propose a novel deep neural network for removing the spot noise from underwater images. Its main idea is to train a generative adversarial network (GAN) to transform the noisy image to clean image. Based on the deep encoder and decoder framework, the skip connections are introduced to combine the features of low-level and high-level to help recover the original image. Meanwhile, the self-attention mechanism is employed to the generative network to capture global dependencies in the feature maps, which can generate the image with fine details at every location. Furthermore, we apply the spectral normalization to both the generative and discriminative networks to stabilize the training process. Experiments evaluated on synthetic and real-world images show that the proposed method outperforms many recent state-of-the-art methods in terms of quantitative and visual quality. Besides, the results also demonstrate that the proposed method has the good ability to remove the spot noise from underwater images while preserving sharp edge and fine details.     

一种软件体系结构级测试序列生成技术

软件体系结构(software architecture,SA)通过对系统构件及其交互的抽象,提供了一个描述大型、复杂系统的高层次模型,软件体系结构的动态描述常被用来指导分析和测试.本文通过CHAM(chemical abstract ma-chine,CHAM)描述的SA规格说明生成LTS,并根据测试需求进行测试功能的选取,提出了基于功能的最小LTS图(M-LTS)生成方法,根据McCabe覆盖方法生成M-LTS图的测试路径.最后以B/S结构为例,验证了该方法在生成SA级的测试路径上是可行的.     

Sample selection for visual domain adaptation via sparse coding

Visual domain adaptation has attracted much attention and has made great achievement in recent years. It deals with the problem of distribution divergence between source and target domains. Current methods mostly focus on transforming images from different domains into a common space to minimize the distribution divergence. However, there are many irrelevant source samples for target domain even after the transformation. In order to eliminate the irrelevant samples, we develop a sample selection algorithm using sparse coding theory. We do the sample selection in a common subspace of source and target data to find as many as relevant source samples. In the common subspace, data characteristics are preserved by using graph regularization. Therefore, we can select the most relevant samples for our target image classification task. Moreover, in order to build a discriminative classifier for the target domain, we use not only the common part of source and target domains learned in the common subspace but also the specific part of target domain. The algorithm can be extended to handle samples from multiple source domains. Experimental results show that our visual domain adaptation method on the image classification tasks can be very effective for the state-of-the-art datasets.     

基于非负矩阵分解的彩色图像质量评价方法

针对稀疏表示的图像质量评价模型都基于灰度图像,缺少颜色信息,该文提出一种基于非负矩阵分解(NMF)的全参考彩色图像质量评价方法。首先,从自然彩色图像中随机采样,得到训练样本,利用非负矩阵分解,训练得到特征基矩阵,并经过Schmidt正交化,构建特征提取矩阵;其次,根据视觉显著性模型,利用最大视觉显著性和显著性差值两步骤选取视觉重要区域;最后,利用特征提取矩阵,得到低维的特征向量,并最终得到彩色图像质量评价值。实验结果表明,该文方法在LIVE, CSIQ和TID2008 3个图像质量评价库上有很好的表现。3个图像库的平均结果显示,该文方法的综合表现优于所有对比方法。这表明该文方法与主观感知有更好的关联度。     

An Intelligent Testing System Embedded With an Ant-Colony-Optimization-Based Test Composition Method

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person's skill. This paper develops a novel intelligent testing system for both teachers and students. Based on the browser/server structure, the proposed testing system comprises a question bank and five modules, offering the features of self-adaptation, reliability, and flexibility for generating parallel tests with identical test ability. The core of the developed system is the ant-colony-optimization-based test composition (ACO-TC) method, which aims at generating high-quality tests for examinations and satisfying multiple requirements. As an advanced computational intelligence algorithm, the proposed ACO-TC method uses a colony of ants to select appropriate questions from a question bank to construct solutions. Pheromone and heuristic information is designed for facilitating the ants' selection. The system is analyzed by composing tests in different situations. The generated tests not only match the expected total completion time, the concept proportions, the average difficulty, and the score proportions of different question types, but also have high average discrimination degrees of questions. The experimental results also show that the system can always generate high-quality tests from question banks with various sizes.