[1] [1] GANDO G, YAMADA T, SATO H, et al. Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs[J]. Expert Systems with Applications, 2016, 66: 295 – 301.

[2] [2] MCCLURE P, KRIEGESKORTE N. Representational distance learning for deep neural networks[J]. Frontiers in Computational Neuroscience, 2016, 10: 131.

[3] [3] QU Y H, YU H, GONG X J, et al. On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach[J]. PLoS One, 2017, 12(12): e0188129.

[4] [4] HAN Y, KIM D. Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction[J]. BMC Bioinformatics, 2017, 18: 585.

[5] [5] ASAKURA T, DATE Y, KIKUCHI J. Application of ensemble deep neural network to metabolomics studies[J]. Analytica Chimica Acta, 2018, 1037: 230 – 236.

[6] [6] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning internal representations by error propagation[M]//RUMENHART D E, MCCELLAND J L. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge: MIT Press, 1986: 318. 362.

[7] [7] CUN Y L, BOSER B, DENKER J S, et al. Handwritten digit recognition with a back-propagation network[C]// Proceedings of the 2nd international conference on neural information processing systems. Cambridge: MIT Press, 1990: 396. 404.

[8] [8] ZHANG K, ZUO W M, CHEN Y J, et al. Beyond a Gaussian Denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142 – 3155.

[9] [9] NOH H, HONG S, HAN B. Learning deconvolution Network for Semantic Segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015.