具有类间差异约束的多对抗深度域适应模型

为实现目标域样本能够与源域中同类样本准确对齐,并在保证样本准确识别率的条件下进一步提高不同类别样本特征间的可区分性,提出了一种带有类间差异约束的域适应模型。首先,该模型采用深度卷积神经网络对源域样本进行了有监督学习,并在训练过程中基于提出的类间差异测量函数对源域样本特征加以类间差异性约束;其次,该模型采用了多对抗域鉴别网络结构,其中提出了一种目标域样本伪标签计算方法,从而将无标签的样本指定到合理的域鉴别网络进行训练;最后,通过最小化分类损失与最大化域鉴别损失,获得最优特征提取器与特征分类器。实验结果表明,对于4种数据集,提出的模型在目标域上平均识别准确率可以达到0.860,同类间的平均距离、不同类间的平均距离、目标域中样本错误识别率相对于改进前分别降低0.003,提升0.065,降低0.025,从而验证了提出模型的性能得到了明显提升。

基于多对抗域适应网络的多工况故障诊断

针对机械设备在多工况下采集到的数据存在分布差异、分类精度较低且人工标注成本较高的问题,提出了小波时频图与迁移学习的多对抗域适应网络(Multi-Adversarial Domain Adaptation,MADA)结合的智能故障诊断方法(CWT-MADA),用于多工况的故障诊断。该方法构建双流深度卷积神经网络学习源域和目标域的原始信息和时频图特征,该组合特征有助于解决故障特征利用不充分的问题;通过源域样本聚类对目标域样本进行打伪标记,并在多对抗域适应过程中约束特征提取器,不断拉近不同域中同类之间的距离,减少工况变换造成的分布差异;与几个目前先进的领域自适应方法进行了对比实验,实验结果表明,CWT-MADA方法在多工况故障诊断中具有较高的准确率。

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.