协作式生成对抗网络

张龙; 赵杰煜; 叶绪伦; 董伟

doi:10.16383/j.aas.2018.c170483

协作式生成对抗网络

doi: 10.16383/j.aas.2018.c170483

宁波大学信息科学与工程学院宁波 315211

基金项目:

浙江省自然科学基金 LZ16F030001

浙江省国际合作项目 2013C24027

国家自然科学基金 61571247

详细信息

作者简介:
张龙  宁波大学博士研究生.2008年获得瑞典布京理工学院硕士学位.主要研究方向为神经网络与深度学习.E-mail:1401082013@nbu.edu.cn

叶绪伦  宁波大学博士研究生.2016年获得宁波大学硕士学位.主要研究方向为非参聚类, 流形学习以及非负矩阵分解.E-mail:1601082017@nbu.edu.cn

董伟  宁波大学硕士研究生.2015年获得辽宁科技大学学士学位.主要研究方向为神经网络, 深度学习.E-mail: 1511082629@nbu.edu.cn

通讯作者:
赵杰煜宁波大学教授.主要研究方向为计算机图像处理, 机器学习, 神经网络.本文通信作者.E-mail:zhao jieyu@nbu.edu.cn

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出版历程
- 收稿日期: 2017-09-07
- 录用日期: 2018-02-07
- 刊出日期: 2018-05-20

Co-operative Generative Adversarial Nets

Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211

Funds:

National Natural Science Foundation of Zhejiang Province LZ16F030001

International Cooperation Projects of Zhejiang Province 2013C24027

National Natural Science Foundation of China 61571247

More Information

Author Bio:
Ph. D. candidate at Ningbo University. He received his master degree from Blekinge Institute of Technology, Sweden in 2008. His research interest covers neural networks and deep learning

Ph. D. candidate at Ningbo University. He received his master degree from Ningbo University in 2016. His research interest covers non-parametric clustering, manifold learning, and non-negative matrix factorization

Master student at Ningbo University. He received his bachelor degree from University of Science and Technology Liaoning in 2015. His research interest covers neural networks and deep learning

Corresponding author: ZHAO Jie-Yu Professor at Ningbo University. His research interest covers computer image processing, machine learning, and neural net-works. Corresponding author of this paper

摘要

摘要: 生成对抗网络（Generative adversarial nets，GANs）将生成模型与判别模型进行了巧妙结合，采用无监督的训练方式，通过相互对抗共同提高，其在学术界掀起了一股新的机器学习热潮.GANs的学习目标是可以完整拟合任意真实样本的数据分布，然而在实际当中，真实样本分布的复杂程度难以预计，容易发生模式坍塌（Mode collapse）等问题，从而导致结果冗余，模型不收敛等.为提高无监督条件下的GANs生成能力，减少或消除模式坍塌，本文提出一种全新的协作式生成网络结构，通过构建多个生成模型，引入协作机制，使得生成模型在训练过程中能够相互学习，共同进步，从而提高模型对真实数据的拟合能力，进一步提高生成质量.通过在三组不同类型的数据集上进行实验，分析对比结果后发现新模型在二维图像生成方面，特别是人脸图片，有着显著的效果，协作机制不仅可以加快模型收敛速度，提高训练效率，还能消除损失函数噪声，在三维模型生成方面也产生了一定的影响.通过调整模型参数，模式坍塌问题也得到了遏制.本文还设计了一种动态学习方法，动态调节模型的学习速率，有效减少了过大或过小的梯度惩罚.
- 生成对抗网络 /
- 协作式 /
- 模式坍塌 /
- 生成模型 /
- 无监督学习
Abstract: Generative adversarial nets (GANs) combine the generative model with the discriminative model. With unsupervised training methods, the two types of models mutually improve through the adversarial process. It sets off a new machine learning boom in academia. The final goal of GANs learning is to fit any real-world data distribution. In practice, however, the real-world data distribution is difficult to estimate. The major problem is mode collapse, which may lead to redundancy and non-convergence. To improve the unsupervised generator and eliminate the risk of mode collapse, this paper proposes a novel co-operative network structure for GANs. Multiple generative models are constructed with a co-operative mechanism. It can help generative models to work together and learn from each other during training. In this way, the fitting ability of generators is largely enhanced, furthermore, the quality of generated data is eventually upgraded. Experiments are conducted on three different types of benchmark datasets. Results show that the new model significantly improves image generation, especially for human face pictures. Additionally, the co-operative mechanism can speed up the convergence, improve network's learning efficiency and deduct loss function noise. It also plays a certain role in 3D model generation and suppress the problem of mode collapse. In order to solve the inconsistency between generation model and discriminative model, a dynamic learning method is developed which can dynamically adjust learning frequency. It ultimately reduces unnecessary gradient penalties.
- Generative adversarial nets (GANs) /
- co-operative /
- mode collapse /
- generative model /
- unsupervised learning
注释:

1) 本文责任编委李力

HTML全文

图 1 生成对抗网络中的模式坍塌问题((a)生成数据分布无法完好拟合真实数据分布; (b)模式坍塌导致生成数据冗余(重复图像过多))

Fig. 1 Mode collapse problem in GANs ((a) synthetic data distribution cannot fit real data distribution in good shape; (b) mode collapse leads to synthetic data redundancy (too many duplicate images))

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图 2 网络结构图

Fig. 2 Network structure

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图 3 本文提出的网络拟合过程

Fig. 3 Fitting process for proposed networks

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图 4 MNIST手写体数据集训练结果(上层采用标准生成对抗网络, 下层采用协作式生成对抗网络)

Fig. 4 Training results on MNIST handwritten digits dataset (upper layer implements standard GANs, lower layer implements co-operative GANs

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图 5 CelebA人脸数据集训练结果(左侧为深度卷积生成对抗网络, 右侧为协作式生成对抗网络, (a)迭代500次; (b)迭代1 000次; (c) $\sim$ (h)迭代1 $\sim$ 6回合)

Fig. 5 Training results on CelebA human faces dataset (left side is trained by DCGAN, right side is trained by ours after, (a) 500 iterations; (b) 1 000 iterations; (c) $\sim$ (h) 1 $\sim$ 6 epochs)

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图 6 CelebA数据集生成结果对比

Fig. 6 Comparison of synthetic data with CelebA dataset

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图 7 判别与生成模型的损失函数值变换情况

Fig. 7 Loss value changes of discriminator and generator models

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图 8 协作式生成对抗网络在ModelNet40数据集的训练结果

Fig. 8 Results by co-operative GANs on ModelNet40 dataset

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