Abstract
The purpose of active learning is to significantly reduce the cost of annotation while ensuring the good performance of the model. In this paper, we propose a novel active learning method based on the combination of pool and synthesis named dual generative adversarial active learning (DGAAL), which includes the functions of image generation and representation learning. This method includes two groups of generative adversarial network composed of a generator and two discriminators. One group is used for representation learning, and then this paper performs sampling based on the predicted value of the discriminator. The other group is used for image generation. The purpose is to generate samples which are similar to those obtained from sampling, so that samples with rich information can be fully utilized. In the sampling process, the two groups of network cooperate with each other to enable the generated samples to participate in sampling process, and to enable the discriminator for sampling to co-evolve. Thus, in the later stage of sampling, the problem of insufficient information for selecting samples based on the pool method is alleviated. In this paper, DGAAL is evaluated extensively on three data sets, and the results show that DGAAL not only has certain advantages over the existing methods in terms of model performance but can also further reduces the annotation cost.
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References
- 1.
Yuan C, Wu Y, Qin X, Qiao S, Pan Y, Huang P, Liu D, Han N (2019) An effective image classification method for shallow densely connected convolution networks through squeezing and splitting techniques. Appl Intell 49(10):3570–3586
- 2.
Lin E, Chen Q, Qi X (2020) Deep reinforcement learning for imbalanced classification. Appl Intell, 1–15
- 3.
Sun C, Shrivastava A, Singh S, Gupta A (2017) Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE international conference on computer vision, pp 843–852
- 4.
Liu P, Zhang H, Eom KB (2016) Active deep learning for classification of hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 10(2):712–724
- 5.
Shao W, Sun L, Zhang D (2018) Deep active learning for nucleus classification in pathology images. In: 2018 IEEE 15th international symposium on biomedical imaging, pp 199–202
- 6.
Sener O, Savarese S (2017) Active learning for convolutional neural networks: A core-set approach. arXiv:1708.00489
- 7.
Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data arXiv:1703.02910
- 8.
Sinha S, Ebrahimi S, Darrell T (2019) Variational adversarial active learning. In: Proceedings of the IEEE international conference on computer vision, pp 5972–5981
- 9.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
- 10.
Arjovsky M, Chintala S, Bottou L (2017) Wasserstein gan. arXiv:1701.07875
- 11.
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
- 12.
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232
- 13.
Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789–8797
- 14.
Tang XL, Du YM, Liu YW, Li JW, Ma YW (2018) Image recognition with conditional deep convolutional generative adversarial networks. J Autom Autom 44(05):855–864
- 15.
Deng J, Cheng S, Xue N, Zhou Y, Zafeiriou S (2018) Uv-gan: adversarial facial uv map completion for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7093–7102
- 16.
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114
- 17.
Freund Y, Seung HS, Shamir E, Tishby N (1997) Selective sampling using the query by committee algorithm. Mach Learn 28(2-3):133–168
- 18.
Gilad-Bachrach R, Navot A, Tishby N (2006) Query by committee made real. In: Advances in neural information processing systems, pp 443–450
- 19.
Dasgupta S, Hsu D (2008) Hierarchical sampling for active learning. In: Proceedings of the 25th international conference on Machine learning, pp 208–215
- 20.
Beluch WH, Genewein T, Nürnberger A, Köhler JM (2018) The power of ensembles for active learning in image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9368–9377
- 21.
Gorriz M, Carlier A, Faure E, Giro-i-Nieto X (2017) Cost-effective active learning for melanoma segmentation. arXiv:1711.091681711.09168
- 22.
Wang K, Zhang D, Li Y, Zhang R, Lin L (2016) Cost-effective active learning for deep image classification. IEEE Trans Circ Syst Video Technol 27(12):2591–2600
- 23.
Dutt Jain S, Grauman K (2016) Active image segmentation propagation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2864–2873
- 24.
Nguyen HT, Smeulders A (2004) Active learning using pre-clustering. In: Proceedings of the twenty-first international conference on machine learning, p 79
- 25.
Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 399–407
- 26.
Kapoor A, Grauman K, Urtasun R, Darrell T (2007) Active learning with gaussian processes for object categorization. In: 2007 IEEE 11th international conference on computer vision, pp 1–8
- 27.
Roy N, McCallum A (2001) Toward optimal active learning through monte carlo estimation of error reduction. ICML, 441–448
- 28.
Ebrahimi S, Elhoseiny M, Darrell T, Rohrbach M (2019) Uncertainty-guided continual learning with bayesian neural networks. arXiv:1906.02425
- 29.
Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2(Nov):45–66
- 30.
Li X, Guo Y (2013) Adaptive active learning for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 859–866
- 31.
Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Proceedings of the 20th international conference on machine learning, pp 59–66
- 32.
Wang Z, Ye J (2015) Querying discriminative and representative samples for batch mode active learning. ACM Trans Knowl Disc Data 9(3):1–23
- 33.
Kuo W, Häne C, Yuh E, Mukherjee P, Malik J (2018) Cost-sensitive active learning for intracranial hemorrhage detection. In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 715–723
- 34.
Melville P, Mooney RJ (2004) Diverse ensembles for active learning. In: Proceedings of the twenty-first international conference on Machine learning, p 74
- 35.
Lv X, Duan F, Jiang JJ, Fu X, Gan L (2020) Deep active learning for surface defect detection. Sensors 20(6):1650
- 36.
Yoo D, Kweon IS (2019) Learning loss for active learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 93–102
- 37.
Zhao Z, Yang X, Veeravalli B, Zeng Z (2020) Deeply supervised active learning for finger bones segmentation. arXiv:2005.03225
- 38.
Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. In: Advances in neural information processing systems, pp 3483–3491
- 39.
Kim K, Park D, Kim KI, Chun SY (2020) Task-aware variational adversarial active learning. arXiv:2002.04709
- 40.
Saquil Y, Kim KI, Hall P (2018) Ranking cgans: Subjective control over semantic image attributes. arXiv:1804.04082
- 41.
Zhang B, Li L, Yang S, Wang S, Zha ZJ, Huang Q (2020) State-relabeling adversarial active learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8756–8765
- 42.
Mahapatra D, Bozorgtabar B, Thiran JP, Reyes M (2018) Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 580–588
- 43.
Mayer C, Timofte R (2020) Adversarial sampling for active learning. In: The IEEE winter conference on applications of computer vision, pp 3071–3079
- 44.
Zhu JJ, Bento J (2017) Generative adversarial active learning. arXiv:1702.07956
- 45.
Tran T, Do TT, Reid I, Carneiro G (2019) Bayesian generative active deep learning. arXiv:1904.11643
- 46.
Gal Y, Islam R, Ghahramani Z (2017) Deep bayesian active learning with image data. arXiv:1703.02910
- 47.
Mottaghi A, Yeung S (2019) Adversarial representation active learning. arXiv:1912.09720
- 48.
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: Advances in neural information processing systems, pp 6626–6637
- 49.
Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784
- 50.
Grosse I, Bernaola-Galván P, Carpena P, Román-Roldán R, Oliver J, Stanley HE (2002) Analysis of symbolic sequences using the jensen-shannon divergence. Phys Rev E 65(4):041905
- 51.
Jin Q, Luo X, Shi Y, Kita K (2019) Image generation method based on improved condition GAN. In: 2019 6th international conference on systems and informatics, pp 1290– 1294
- 52.
Cui S, Jiang Y (2017) Effective lipschitz constraint enforcement for wasserstein GAN training. In: 2017 2nd IEEE international conference on computational intelligence and applications, pp 74–78
- 53.
Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
- 54.
Donahue J, Simonyan K (2019) Large scale adversarial representation learning. In: Advances in neural information processing systems, pp 10542–10552
- 55.
Ma K, Shu Z, Bai X, Wang J, Samaras D (2018) Docunet: Document image unwarping via a stacked u-net. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4709
- 56.
Targ S, Almeida D, Lyman K (2016) Resnet in resnet: Generalizing residual architectures. arXiv:1603.08029
- 57.
DeVries T, Taylor GW (2017) Dataset augmentation in feature space. arXiv:1702.05538
- 58.
Yu B, Zhu DH (2009) Combining neural networks and semantic feature space for email classification. Knowl-Based Syst 22(5):376–381
- 59.
Xue W, Mou X, Zhang L, Feng X (2013) Perceptual fidelity aware mean squared error
- 60.
Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. arXiv:1802.05957
- 61.
Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images
- 62.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255
- 63.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
- 64.
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256
- 65.
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980
- 66.
Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059
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This work was supported by the Basic Scientific Research Projects of Central Universities 2572018BH07 and the Natural Science Foundation of Heilongjiang Province under Grant LH2019C003.
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Guo, J., Pang, Z., Bai, M. et al. Dual generative adversarial active learning. Appl Intell (2021). https://doi.org/10.1007/s10489-020-02121-4
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Keywords
- Deep learning
- Generative adversarial networks
- Image generation
- Active learning