Abstract
Semi-supervised image classification is one of the areas of interest within the computer vision, which can build better classifiers using a few labeled images and plenty of unlabeled images. Recently, semi-supervised image classification methods based on the generative adversarial network (GAN) get promising results. In this paper, we introduce a self-attention mechanism to propose an attention-based GAN for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. We also adopt manifold regularization as an additional regularization term so that we can make the most of the unlabeled images. We test the proposed method on SVHN and CIFAR-10 datasets. The experimental results show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan. arXiv preprint arXiv:1701.07875
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7(Nov):2399–2434
Berthelot D, Schumm T, Metz L (2017) Began: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717
Caron M, Bojanowski P, Joulin A, Douze M (2018) Deep clustering for unsupervised learning of visual features. In: Proceedings of the european conference on computer vision (ECCV), pp 132–149
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems, pp 2172–2180
Dai Z, Yang Z, Yang F, Cohen WW, Salakhutdinov RR (2017) Good semi-supervised learning that requires a bad gan. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30, Curran Associates, Inc., pp 6510–6520, http://papers.nips.cc/paper/7229-good-semi-supervised-learning-that-requires-a-bad-gan.pdf
Dong J, Gao K, Chen X, Cao J (2019) Refocused attention: long short-term rewards guided video captioning. Neural Process Lett. https://doi.org/10.1007/s11063-019-10030-y
Fan R, Zhou P, Chen W, Jia J, Liu G (2018) An online attention-based model for speech recognition. arXiv preprint arXiv:1811.05247
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27, Curran Associates, Inc., pp 2672–2680, http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. In: Advances in neural information processing systems, pp 5767–5777
Haeusser P, Plapp J, Golkov V, Aljalbout E, Cremers D (2018) Associative deep clustering: training a classification network with no labels. In: German conference on pattern recognition, Springer, pp 18–32
Han Z, Tao X, Li H, Zhang S, Metaxas D (2017) Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV)
Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30, curran associates, Inc., pp 6626–6637, http://papers.nips.cc/paper/7240-gans-trained-by-a-two-time-scale-update-rule-converge-to-a-local-nash-equilibrium.pdf
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Kosiorek A, Bewley A, Posner I (2017) Hierarchical attentive recurrent tracking. In: Advances in neural information processing systems, pp 3053–3061
Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242
Lecouat B, Foo CS, Zenati H, Chandrasekhar V (2018) Manifold regularization with gans for semi-supervised learning. arXiv preprint arXiv:1807.04307
Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Lee DH (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol 3, p 2
LI C, Xu T, Zhu J, Zhang B (2017) Triple generative adversarial nets. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30, Curran Associates, Inc., pp 4088–4098, http://papers.nips.cc/paper/6997-triple-generative-adversarial-nets.pdf
Li Y, Liu S, Yang J, Yang MH (2017) Generative face completion. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Liu W, Ma X, Zhou Y, Tao D, Cheng J (2018) \( p \)-laplacian regularization for scene recognition. IEEE Trans Cybern 49(8):2927–2940
Luo Y, Zhu J, Li M, Ren Y, Zhang B (2018) Smooth neighbors on teacher graphs for semi-supervised learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8896–8905
Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025
Ma X, Liu W, Li S, Tao D, Zhou Y (2018) Hypergraph \( p \)-laplacian regularization for remotely sensed image recognition. IEEE Trans Geosci Remote Sens 57(3):1585–1595
Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784
Miyato T, Kataoka T, Koyama M, Yoshida Y (2018a) Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957
Miyato T, Maeda Si, Ishii S, Koyama M (2018b) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. In: IEEE transactions on pattern analysis and machine intelligence
Odena A (2016) Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583
Oneto L (2019) Model selection and error estimation in a nutshell. Springer, Berlin
Qi GJ, Zhang L, Hu H, Edraki M, Wang J, Hua XS (2018) Global versus localized generative adversarial nets. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1517–1525
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434
Rao Y, Lu J, Zhou J (2017) Attention-aware deep reinforcement learning for video face recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3931–3940
Rasmus A, Berglund M, Honkala M, Valpola H, Raiko T (2015) Semi-supervised learning with ladder networks. In: Advances in neural information processing systems, pp 3546–3554
Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, Chen X (2016) Improved techniques for training gans. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems 29, Curran Associates, Inc., pp 2234–2242, http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf
Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112
Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in neural information processing systems, pp 1195–1204
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156–3164
Wang T, Hu H, He C (2019) Image caption with endogenous-exogenous attention. Neural Process Lett. https://doi.org/10.1007/s11063-019-09979-7
Wei X, Gong B, Liu Z, Lu W, Wang L (2018) Improving the improved training of wasserstein gans: a consistency term and its dual effect. arXiv preprint arXiv:1803.01541
Woo S, Park J, Lee JY, So Kweon I (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19
Yu J, Rui Y, Tang YY, Tao D (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442
Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. In: IEEE transactions on pattern analysis and machine intelligence
Zhai M, Xiang X, Zhang R, Lv N, El Saddik A (2019) Ad-net: attention guided network for optical flow estimation using dilated convolution. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 2207–2211
Zhang H, Goodfellow I, Metaxas D, Odena A (2018a) Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318
Zhang Z, Zhao M, Chow TW (2013) Graph based constrained semi-supervised learning framework via label propagation over adaptive neighborhood. IEEE Trans Knowl Data Eng 27(9):2362–2376
Zhang Z, Li F, Jia L, Qin J, Zhang L, Yan S (2017) Robust adaptive embedded label propagation with weight learning for inductive classification. IEEE Trans Neural Netw Learn Syst 29(8):3388–3403
Zhang Z, Jia L, Zhao M, Liu G, Wang M, Yan S (2018b) Kernel-induced label propagation by mapping for semi-supervised classification. IEEE Trans Big Data 5(2):148–165
Zhang Z, Zhang Y, Liu G, Tang J, Yan S, Wang M (2019) Joint label prediction based semi-supervised adaptive concept factorization for robust data representation. In: IEEE Transactions on Knowledge and Data Engineering
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE international conference on computer vision (ICCV)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported in part by the National Natural Science Foundation of China under Grant 61401113, in part by the Natural Science Foundation of Heilongjiang Province of China under Grant LC201426, and in part by the Fundamental Research Funds for the Central Universities of China under Grant 3072019CF0801.
Rights and permissions
About this article
Cite this article
Xiang, X., Yu, Z., Lv, N. et al. Attention-Based Generative Adversarial Network for Semi-supervised Image Classification. Neural Process Lett 51, 1527–1540 (2020). https://doi.org/10.1007/s11063-019-10158-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-019-10158-x