Multi-view Learning and Reconstruction Algorithms via Generative Adversarial Networks
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摘要: 同一事物通常需要从不同角度进行表达.然而，现实应用经常引出复杂的场景，导致完整视图数据很难获得.因此研究如何构建事物的完整视图具有重要意义.本文提出一种基于生成对抗网络（Generative adversarial networks，GAN）的多视图学习与重构算法，利用已知单一视图，通过生成式方法构建其他视图.为构建多视图通用的表征，提出新型表征学习算法，使得同一实例的任意视图都能映射至相同的表征向量，并保证其包含实例的重构信息.为构建给定事物的多种视图，提出基于生成对抗网络的重构算法，在生成模型中加入表征信息，保证了生成视图数据与源视图相匹配.所提出的算法的优势在于避免了不同视图间的直接映射，解决了训练数据视图不完整问题，以及构造视图与已知视图正确对应问题.在手写体数字数据集MNIST，街景数字数据集SVHN和人脸数据集CelebA上的模拟实验结果表明，所提出的算法具有很好的重构性能.Abstract: Generally, objects often require to represent in different views. However, real-world applications in complex scenarios can hardly have complete views of a given object. In this paper, we propose generative adversarial network (GAN) based multi-view learning and reconstruction algorithms. A novel representation learning algorithm is proposed, which guarantees different views of the same object are mapped to the same representation. Meanwhile, the algorithm guarantees the representation carries enough reconstructed information. To construct multi-views of a given object, a generative adversarial network based reconstruction algorithm is proposed, which includes the representation information in the generation and discrimination models to guarantee the constructed views perfectly map the source view. The merits of the proposed algorithms lie in the fact that they avoid direct mapping among different views, and can solve the problem of missing views in training data and the problem of mapping between constructed views and the source views. Simulated experiments on handwritten digit dataset (MNIST), street view house numbers dataset (SVHN) and CelebFaces attributes dataset (CelebA) indicate that the proposed algorithms yield satisfactory reconstruction performances.1) 本文责任编委 王坤峰
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图 2 原始视图数据$x$, 表征向量$\pmb c$, 重构视图数据$\hat{x}$间的互信息示意图
Fig. 2 Schematic diagram of mutual information among original view data $x$, representative vector $\pmb c$, reconstructed data $\hat{x}$
图 3 基于生成对抗网络的多视图数据生成框架
Fig. 3 Framework of the generative adversarial network based multi-view data generation
图 5 以视图2为源数据在MNIST上的重构结果
Fig. 5 Reconstruction results that take view 2 as source data on MNIST
图 6 以视图3为源数据在MNIST上的重构结果
Fig. 6 Reconstruction results that take view 3 as source data on MNIST
图 7 以视图2为源数据在SVHN上的重构结果
Fig. 7 Reconstruction results that take view 2 as source data on SVHN
图 8 以视图3为源数据在SVHN上的重构结果
Fig. 8 Reconstruction results that take view 3 as source data on SVHN
图 9 以视图2为源数据在CelebA上的重构结果
Fig. 9 Reconstruction results that take view 2 and view 3 as source data respectively on CelebA
表 1 MNIST数据集上的SSIM和PSNR比较结果
Table 1 Comparison results of SSIM and PSNR on MNIST
算法 SSIM值 PSNR值(dB) MVGAN (视图2重构视图1) 0.8520±0.0001 16.3135±0.0880 MVGAN (视图3重构视图1) 0.6474±0.0013 12.2109±0.1442 CGAN 0.7414±0.0001 12.0301±0.0512 CVAE 0.7912±0.0031 12.1184±0.0013 
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表 2 SVHN数据集上的SSIM和PSNR比较结果
Table 2 Comparison results of SSIM and PSNR on SVHN
算法 SSIM值 PSNR值(dB) MVGAN (视图2重构视图1) 0.4140±0.0022 18.7987±0.1475 MVGAN (视图3重构视图1) 0.1848±0.0020 15.8026±0.1306 CGAN 0.3357±0.0017 14.8910±0.0002 CVAE 0.3465±0.0028 15.0137±0.0071
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表 3 CelebA视图2和视图3对应选中的10维属性
Table 3 The chosen attributes for view 2 and view 3 (10 dimensions)
图片编号 秃顶 刘海 黑发 眼镜 男性 嘴微张 窄眼 无胡须 苍白肤色 戴帽 a -1 -1 -1 1 1 -1 -1 -1 -1 -1 b -1 -1 1 -1 1 -1 -1 -1 -1 -1 c -1 -1 1 -1 1 -1 -1 1 -1 -1 d -1 -1 1 -1 1 1 -1 1 -1 -1 e -1 -1 -1 -1 -1 -1 -1 1 -1 -1 f -1 -1 -1 -1 -1 -1 -1 1 1 -1 g -1 -1 -1 -1 1 -1 -1 1 -1 -1 h -1 -1 -1 -1 -1 1 -1 1 -1 -1 i -1 -1 -1 -1 -1 -1 -1 1 -1 -1 j -1 -1 -1 -1 1 1 1 1 1 -1 k -1 1 -1 -1 1 -1 -1 1 -1 -1 l -1 -1 -1 -1 -1 1 -1 1 -1 -1 m -1 -1 -1 -1 -1 1 -1 1 -1 -1 n -1 -1 -1 -1 1 1 -1 -1 -1 -1 o -1 1 1 -1 1 1 -1 1 -1 -1
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表 4 CelebA数据集上的SSIM和PSNR比较结果
Table 4 Comparison results of SSIM and PSNR on CelebA
算法 SSIM值 PSNR值(dB) MVGAN (视图2重构视图1) 0.1143±0.0023 10.0574±0.0605 MVGAN (视图3重构视图1) 0.1132±0.0022 10.0342±0.0587 CGAN 0.0512±0.0036 9.5312±0.0012 CVAE 0.0716±0.0058 9.7881±0.0020
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