基于半监督学习生成对抗网络的人脸还原算法研究

曹志义; 牛少彰; 张继威

doi:10.11999/JEIT170357

基于半监督学习生成对抗网络的人脸还原算法研究

doi: 10.11999/JEIT170357

基金项目:

国家自然科学基金(61370195, U1536121)

计量
- 文章访问数: 1442
- HTML全文浏览量: 199
- PDF下载量: 274
- 被引次数: 0
出版历程
- 收稿日期: 2017-04-20
- 修回日期: 2017-10-17
- 刊出日期: 2018-02-19

Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning

Funds:

The National Natural Science Foundation of China (61370195, U1536121)

摘要

摘要: 基于大量训练样本生成高置信度图像的生成对抗网络研究已经取得一些成果，但是现有的研究只针对已知训练样本进行图像生成，而未将训练的参数用于训练样本之外的图像生成。该文设计了一种改进的生成对抗网络模型，在已有网络的基础上增加一个还原层，使得测试图像可以通过改进的对抗网络生成对应的高置信度图像。实验结果表明，改进的生成对抗网络参数可以应用到训练集之外的普通样本。同时本文改进了生成模型的损失算法，极大地缩短了网络的收敛时间。
- 生成对抗网络 /
- 半监督学习 /
- 生成模型 /
- 损失函数
Abstract: Based on a large number of training samples to generate high confidence images, generative adversarial nets achieve good results, but the existing network of image generation in the training sample basis, the training parameters can not be used to generate images outside of training samples. In this paper, an improved generative adversarial nets model is proposed, and a reduction layer is added on the basis of the existing network, so that the test image can generate the corresponding high confidence image through the improved generative adversarial nets. The experimental results show that the improved generative adversarial nets parameters can be applied to the common samples outside the training set. At the same time, this paper improves the loss algorithm of the generated model, which greatly shortens the convergence time of the network.
- Generative adversarial nets /
- Semisupervised learning /
- Generative model /
- Loss function

HTML全文

参考文献(21)

NGUYEN A, YOSINSKI J, and CLUNE J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 427-436. doi: 10.1109/CVPR.2015. 7298640.

SZEGEDY C, ZAREMBA W, SUTSKEVER I, et al. Intriguing properties of neural networks[J]. CoRR, 2013, 12(6199): 1-6.

GOODFELLOW I J, POUGETABADIE J, MIRZA M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014, 3: 2672-2680.

RADFORD A, METZ L, and CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. CoRR, 2015, 11(06434): 1-7.

DENG Jia, DONG Wei, SOCHER R, et al. Imagenet: A large-scale hierarchical image database[C]. Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, 2009, 248-255. doi: 10.1109/CVPR.2009.5206848.

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, Doha, Qatar, 2012: 1097-1105.

GOODFELLOW I J, SHLENS J, and SZEGEDY C. Explaining and harnessing adversarial examples[J]. CoRR, 2014, 12(6572): 1-7.

RASMUS A, VALPOLA H, et al. Semisupervised learning with ladder network[J]. CoRR, 2015, 7(2672): 1-7.

IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. CoRR, 2015, 2(3167): 1-9.

DOSOVITSKIY A, FISCHER P, SPRINGENBERG J T, et al. Discriminative unsupervised feature learning with exemplar convolutional neural net-works[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38: 1734-1747. doi: 10.1109/ TPAMI.2015.2496141.

KINGMA D P and BA J L. Adam: A method for stochastic optimization[J]. CoRR, 2014, 12(6980 ): 1-6.

HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.

ARJOVSKY M and BOTTOU L. Towards principled methods for training generative adversarial networks[J]. CoRR, 2017, 1(4862): 1-8.

ODENA A, OLAH C, and SHLENS J. Conditional image synthesis with auxiliary classifier gans[J]. CoRR, 2016, 10(9585): 1-8.

WANG X, SHRIVASTAVA A, and GUPTA A. A-fast- RCNN: Hard positive generation via adversary for object detection[J]. CoRR, 2017, 4(3414): 1-6.

ARJOVSKY M, CHINTALA S, and BOTTOU L. Wasserstein gan[J]. CoRR, 2017, 1(7875): 1-7.

GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[J]. CoRR, 2017, 4(0028): 1-8.

HU H and HAAN G D. Low cost robust blur estimator[C]. IEEE International Conference on Image Processing. San Antinio, TX, 2007: 617-620.

AHONEN T, RAHTU E, OJANSIVU V, et al. Recognition of blurred faces using local phase quantization[C]. IEEE International Conference on Pattern Recognition, Tampa, Florida, USA, 2008: 1-4.

NISHIYAMA M, TAKESHIMA H, SHOTTON J, et al. Facial deblur inference to improve recognition of blurred faces[C]. IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA, 2009: 1115-1122.

SWAMINATH A, MAO Y, and WU M. Robust and secure image hashing[J]. IEEE Transactions on Information Forensics Security, 2013, 1(2): 215-230.

施引文献

资源附件(0)

访问统计