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The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance.
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Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-sec: deep learning in android malware detection,” in ACM SIGCOMM Computer Communication Review , vol. 44, no. 4, 2014, pp. 371–372
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Adv. in Neural Information Processing Systems , 2014, pp. 2672–2680
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2015
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S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,” PloS one , vol. 10, no. 7, p. e0130140, 2015
2015
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
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Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in International Conference on Computer Vision , 2015, pp. 3730–3738
2015
Cited alongside, same era.
M. A. Alsheikh, D. Niyato, S. Lin, H.-P. Tan, and Z. Han, “Mobile big data analytics using deep learning and Apache Spark,” IEEE Network , vol. 30, no. 3, pp. 22–29, 2016
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Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning , vol. 48, 2016, pp. 1050–1059
2016
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2016
Cited alongside, same era.
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture , vol. 147, pp. 70–90, 2018
2018
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2018
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Z. C. Lipton, “The mythos of model interpretability,” Queue , vol. 16, no. 3, pp. 31–57, 2018
2018
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J. Del Ser, E. Osaba, J. J. Sanchez-Medina, and I. Fister, “Bioinspired computational intelligence and transportation systems: a long road ahead,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
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A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry 4.0,” Information Fusion , vol. 50, pp. 92–111, 2019
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M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International Conference on Machine Learning , 2017, pp. 214–223
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Hindupur, “The GAN zoo: A list of all named GANs,” 2017
2017
Cited alongside, same era.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in IEEE International Conference on Computer Vision , 2017, pp. 5907–5915
2017
Cited alongside, same era.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Adv. in Neural Information Processing Systems , 2017, pp. 4765–4774
2017
Cited alongside, same era.
2017
Cited alongside, same era.
P. Lyu, X. Bai, C. Yao, Z. Zhu, T. Huang, and W. Liu, “Auto-encoder guided GAN for chinese calligraphy synthesis,” in IEEE International Conference on Document Analysis and Recognition , vol. 1, 2017, pp. 1095–1100
2017
Cited alongside, same era.
2019
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H. Wu, S. Zheng, J. Zhang, and K. Huang, “Gp-gan: Towards realistic high-resolution image blending,” in ACM International Conference on Multimedia , 2019, pp. 2487–2495
2019
Later among the works it cites.
Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, “Attgan: Facial attribute editing by only changing what you want,” IEEE Transactions on Image Processing , vol. 28, no. 11, pp. 5464–5478, 2019
2019
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2019
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A. Benitez-Hidalgo, A. J. Nebro, J. Garcia-Nieto, I. Oregi, and J. Del Ser, “jMetalPy: a Python framework for multi-objective optimization with metaheuristics,” Swarm and Evolutionary Computation , vol. 51, p. 100598, 2019
2019
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A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, and F. Herrera, “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion , vol. 58, pp. 82–115, 2020
2020
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