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基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究

曹志義 牛少彰 張繼威

曹志義, 牛少彰, 張繼威. 基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 323-330. doi: 10.11999/JEIT170357
引用本文: 曹志義, 牛少彰, 張繼威. 基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 323-330. doi: 10.11999/JEIT170357
CAO Zhiyi, NIU Shaozhang, ZHANG Jiwei. Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning[J]. Journal of Electronics & Information Technology, 2018, 40(2): 323-330. doi: 10.11999/JEIT170357
Citation: CAO Zhiyi, NIU Shaozhang, ZHANG Jiwei. Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning[J]. Journal of Electronics & Information Technology, 2018, 40(2): 323-330. doi: 10.11999/JEIT170357

基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究

doi: 10.11999/JEIT170357
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61370195, U1536121)

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: 基于大量訓(xùn)練樣本生成高置信度圖像的生成對(duì)抗網(wǎng)絡(luò)研究已經(jīng)取得一些成果,但是現(xiàn)有的研究只針對(duì)已知訓(xùn)練樣本進(jìn)行圖像生成,而未將訓(xùn)練的參數(shù)用于訓(xùn)練樣本之外的圖像生成。該文設(shè)計(jì)了一種改進(jìn)的生成對(duì)抗網(wǎng)絡(luò)模型,在已有網(wǎng)絡(luò)的基礎(chǔ)上增加一個(gè)還原層,使得測(cè)試圖像可以通過(guò)改進(jìn)的對(duì)抗網(wǎng)絡(luò)生成對(duì)應(yīng)的高置信度圖像。實(shí)驗(yàn)結(jié)果表明,改進(jìn)的生成對(duì)抗網(wǎng)絡(luò)參數(shù)可以應(yīng)用到訓(xùn)練集之外的普通樣本。同時(shí)本文改進(jìn)了生成模型的損失算法,極大地縮短了網(wǎng)絡(luò)的收斂時(shí)間。
  • 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.
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出版歷程
  • 收稿日期:  2017-04-20
  • 修回日期:  2017-10-17
  • 刊出日期:  2018-02-19

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