计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 202-209.doi: 10.11896/jsjkx.200400083
邢红杰, 郝忠
XING Hong-jie, HAO ZhongHebei
摘要: 生成式对抗神经网络(Generative Adversarial Nets,GAN)和对抗自编码器(Adversarial Autoencoder,AAE)被成功地应用于图像生成中。此外,对抗网络能够无监督地对样本中所包含的数据特征进行学习。然而,将传统的对抗网络应用于异常检测时取得的分类效果较差,有两个方面的原因:一是GAN属于生成式模型,但异常检测模型往往被归入判别式模型的范畴;二是现有的AAE以自编码器的中间向量作为判别输入,对数据的重构效果不够理想。基于此,提出了一种基于双判别器的AAE,并将其应用于解决异常检测问题。所提方法中的双判别器具有不同的判别能力,即局部判别能力和全局判别能力。在MNIST,Fashion-MNIST和CIFAR-10数据集上的实验结果表明,所提方法能够有效避免训练过程中出现模式崩溃的问题。此外,与相关方法进行对比,所提方法取得了更优的检测性能。
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