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Yong-chuan Tang

http://orcid.org/0000-0002-0157-7771

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.12 P.1595-1617

http://doi.org/10.1631/FITEE.1900398


A review of design intelligence: progress, problems, and challenges


Author(s):  Yong-chuan Tang, Jiang-jie Huang, Meng-ting Yao, Jia Wei, Wei Li, Yong-xing He, Ze-jian Li

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yctang@zju.edu.cn

Key Words:  Design intelligence, Creativity, Personas, Ideation, AI-generated content, Computational aesthetics


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Yong-chuan Tang, Jiang-jie Huang, Meng-ting Yao, Jia Wei, Wei Li, Yong-xing He, Ze-jian Li. A review of design intelligence: progress, problems, and challenges[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(12): 1595-1617.

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publisher="Zhejiang University Press & Springer",
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Abstract: 
design intelligence is an important branch of artificial intelligence (AI), focusing on the intelligent models and algorithms in creativity and design. In the context of AI 2.0, studies on design intelligence have developed rapidly. We summarize mainly the current emerging framework of design intelligence and review the state-of-the-art techniques of related topics, including user needs analysis, ideation, content generation, and design evaluation. Specifically, the models and methods of intelligence-generated content are reviewed in detail. Finally, we discuss some open problems and challenges for future research in design intelligence.

设计智能研究综述:进展、问题和挑战

摘要:设计智能是人工智能重要分支,专注于创意和设计中的智能模型和算法。在人工智能2.0背景下,设计智能研究发展迅速。本文首先介绍设计智能研究背景,提出设计智能研究的理论框架。从用户需求分析、创意激发、内容生成和设计评价4个维度,详细综述设计智能研究进展和最新技术;重点论述关于智能生成内容的模型和方法。最后,提出未来设计智能研究中的开放问题和挑战。

关键词:设计智能;创造力;用户画像;创意激发;智能生成内容;计算美学

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Arjovsky M, Chintala S, Bottou L, 2017. Wasserstein generative adversarial networks. Proc 34th Int Conf on Machine Learning, p.298-321.

[2]Aubry M, Maturana D, Efros AA, et al., 2014. Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3762-3769.

[3]Ballester C, Bertalmio M, Caselles V, et al., 2001. Filling-in by joint interpolation of vector fields and gray levels. IEEE Trans Image Process, 10(8):1200-1211.

[4]Bertalmio M, Sapiro G, Caselles V, et al., 2000. Image inpainting. Proc 27th Annual Conf on Computer Graphics and Interactive Techniques, p.417-424.

[5]Bharadhwaj H, Park H, Lim BY, 2018. RecGAN: recurrent generative adversarial networks for recommendation systems. Proc 12th ACM Conf on Recommender Systems, p.372-376.

[6]Boden MA, 2009. Computer models of creativity. AI Mag, 30(3):23-34.

[7]Brock A, Donahue J, Simonyan K, 2018. Large scale GAN training for high fidelity natural image synthesis. https://arxiv.org/abs/1809.11096

[8]Bruna J, Sprechmann P, LeCun Y, 2015. Super-resolution with deep convolutional sufficient statistics. https://arxiv.org/abs/1511.05666

[9]Chakrabarti A, Siddharth L, Dinakar M, et al., 2017. Idea inspire 3.0—a tool for analogical design. In: Chakrabarti A, Chakrabarti D (Eds.), Research into Design for Communities. Springer, Singapore, p.475-485.

[10]Champandard AJ, 2016. Semantic style transfer and turning two-bit doodles into fine artworks. https://arxiv.org/abs/1603.01768

[11]Chan C, Ginosar S, Zhou TH, et al., 2018. Everybody dance now. https://arxiv.org/abs/1808.07371

[12]Chen DD, Yuan L, Liao J, et al., 2018. Stereoscopic neural style transfer. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.6654-6663.

[13]Chen LQ, Wang P, Dong H, et al., 2019. An artificial intelligence based data-driven approach for design ideation. J Vis Commun Image Represent, 61:10-22.

[14]Ciesielski V, Barile P, Trist K, 2013. Finding image features associated with high aesthetic value by machine learning. Proc 2nd Int Conf on Evolutionary and Biologically Inspired Music, Sound, Art and Design, p.47-58.

[15]Cooper A, 1999. The Inmates Are Running the Asylum. SAMS, Indianapolis, USA.

[16]Cooper A, Reimann RM, 2003. About Face 2.0: the Essentials of Interaction Design. John Wiley & Sons, Indianapolis, USA.

[17]Dash A, Gamboa JCB, Ahmed S, et al., 2017. TAC-GAN-text conditioned auxiliary classifier generative adversarial network. https://arxiv.org/abs/1703.06412

[18]Datta R, Joshi D, Li J, et al., 2006. Studying aesthetics in photographic images using a computational approach. Proc 9th European Conf on Computer Vision, p.288-301.

[19]de Góez Silva Garza A, Maher ML, 1999. An evolutionary approach to case adaptation. Proc 3rd Int Conf on Case-Based Reasoning, p.162-173.

[20]de Silva Garza AG, 2019. An introduction to and comparison of computational creativity and design computing. Artif Intell Rev, 51(1):61-76.

[21]Deng J, Dong W, Socher R, et al., 2009. ImageNet: a large-scale hierarchical image database. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.248-255.

[22]Deng YB, Loy CC, Tang XO, 2018. Aesthetic-driven image enhancement by adversarial learning. Proc 26th ACM Int Conf on Multimedia, p.870-878.

[23]Donahue J, Krähenbühl P, Darrell T, 2016. Adversarial feature learning. https://arxiv.org/abs/1605.09782

[24]Dou Q, Zheng XS, Sun TF, et al., 2019. Webthetics: quantifying webpage aesthetics with deep learning. Int J Hum Comput Stud, 124:56-66.

[25]Dugosh KL, Paulus PB, Roland EJ, et al., 2000. Cognitive stimulation in brainstorming. J Pers Soc Psychol, 79(5):722-735.

[26]Dumoulin V, Visin F, 2016. A guide to convolution arithmetic for deep learning. https://arxiv.org/abs/1603.07285

[27]Edelman RR, Hesselink JR, Zlatkin MB, 1996. MRI: Clinical Magnetic Resonance Imaging. Saunders, Philadelphia.

[28]Efros AA, Freeman WT, 2001. Image quilting for texture synthesis and transfer. Proc 28th Annual Conf on Computer Graphics and Interactive Techniques, p.341-346.

[29]Elgammal A, Liu B, Elhoseiny M, et al., 2017. CAN: creative adversarial networks, generating “art” by learning about styles and deviating from style norms. https://arxiv.org/abs/1706.07068

[30]Fang H, Zhang M, 2017. Creatism: a deep-learning photographer capable of creating professional work. https://arxiv.org/abs/1707.03491

[31]Faste H, Rachmel N, Essary R, et al., 2013. {Brainstorm, chainstorm, cheatstorm, tweetstorm: new ideation strategies for distributed HCI design}. Proc Conf on Human Factors in Computing Systems, p.1343-1352.

[32]Fu K, Murphy J, Yang M, et al., 2015. Design-by-analogy: experimental evaluation of a functional analogy search methodology for concept generation improvement. Res Eng Des, 26(1):77-95.

[33]Garabedian CA, 1934. Birkhoff on aesthetic measure. Bull Amer Math Soc, 40(1):7-10.

[34]Gatys L, Ecker A, Bethge M, 2016a. Image style transfer using convolutional neural networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2414-2423.

[35]Gatys L, Ecker A, Bethge M, 2016b. A neural algorithm of artistic style. J Vis, 16(12):326.

[36]Gero JS, 1990. Design prototypes: a knowledge representation schema for design. AI Mag, 11(4):26-36.

[37]Gilon K, Chan J, Ng FY, et al., 2018. Analogy mining for specific design needs. Proc CHI Conf on Human Factors in Computing Systems, p.121.

[38]Goel AK, Rugaber S, Vattam S, 2009. Structure, behavior, and function of complex systems: the structure, behavior, and function modeling language. AI Edam, 23(1):23-35.

[39]Goldschmidt G, Smolkov M, 2006. Variances in the impact of visual stimuli on design problem solving performance. Des Stud, 27(5):549-569.

[40]Gooch B, Gooch A, 2001. Non-photorealistic Rendering. A K Peters/CRC Press, New York, USA.

[41]Goodfellow I, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27$^rm th$ Int Conf on Neural Information Processing Systems, p.2672-2680.

[42]Grudin J, Pruitt J, 2002. Personas, participatory design, and product development: an infrastructure for engagement. Proc 7th Biennial Participatory Design Conf, p.144-152.

[43]Gulrajani I, Ahmed F, Arjovsky M, et al., 2017. Improved training of Wasserstein GANs. Advances in Neural Information Proc Systems, p.5767-5777.

[44]Han J, Shi F, Chen LQ, et al., 2018. {A computational tool for creative idea generation based on analogical reasoning and ontology}. Artif Intell Eng Des Anal Manuf, 32(4):462-477.

[45]Hao J, Zhou YJ, Zhao QF, et al., 2019. An evolutionary computation based method for creative design inspiration generation. J Intell Manuf, 30(4):1673-1691.

[46]Hartson R, Pyla PS, 2012. The UX Book: Process and Guidelines for Ensuring a Quality User Experience. Elsevier, Amsterdam.

[47]He KM, Sun J, 2014. Image completion approaches using the statistics of similar patches. IEEE Trans Patt Anal Mach Intell, 36(12):2423-2435.

[48]Hertzmann A, Jacobs CE, Oliver N, et al., 2001. Image analogies. Proc 28th Annual Conf on Computer Graphics and Interactive Techniques, p.327-340.

[49]Hong YJ, Hwang U, Yoo J, et al., 2019. How generative adversarial networks and their variants work: an overview. ACM Comput Surv, 52(1):10.

[50]Huang HZ, Wang H, Luo WH, et al., 2017. Real-time neural style transfer for videos. IEEE Conf on Computer Vision and Pattern Recognition, p.7044-7052.

[51]Huang X, Belongie S, 2017. Arbitrary style transfer in real-time with adaptive instance normalization. Proc IEEE Int Conf on Computer Vision, p.1501-1510.

[52]Iizuka S, Simo-Serra E, Ishikawa H, 2017. Globally and locally consistent image completion. ACM Trans Graph, 36(4), Article 107.

[53]Isola P, Zhu JY, Zhou TH, et al., 2017. Image-to-image translation with conditional adversarial networks. IEEE Conf on Computer Vision and Pattern Recognition, p.5967-5976.

[54]Jansen BJ, Jung SG, Salminen J, et al., 2017. Viewed by too many or viewed too little: using information dissemination for audience segmentation. Proc Assoc Inform Sci Technol, 54(1):189-196.

[55]Jansson DG, Smith SM, 1991. Design fixation. Des Stud, 12(1):3-11.

[56]Jia J, Huang J, Shen GY, et al., 2016. Learning to appreciate the aesthetic effects of clothing. Proc 30th AAAI Conf on Artificial Intelligence, p.1216-1222.

[57]Jia L, Becattini N, Cascini G, et al., 2020. Testing ideation performance on a large set of designers: effects of analogical distance. Int J Des Creat Innov, 8(1):31-45.

[58]Jiang SH, Fu Y, 2017. Fashion style generator. Proc 26th Int Joint Conf on Artificial Intelligence, p.3721-3727.

[59]Jing YC, Yang YZ, Feng ZL, et al., 2019. Neural style transfer: a review. IEEE Trans Vis Comput Graph, in press.

[60]Jo Y, Park J, 2019.

[61]SC-FEGAN: face editing generative adversarial network with user‘s sketch and color. https://arxiv.org/abs/1902.06838

[62]Johnson J, Alahi A, Li FF, 2016. Perceptual losses for real-time style transfer and super-resolution. Proc 14th European Conf, p.694-711.

[63]Karras T, Laine S, Aila T, 2019. A style-based generator architecture for generative adversarial networks. The IEEE Conf on Computer Vision and Pattern Recognition, p.4401-4410.

[64]Kaufman JC, Sternberg RJ, 2006. The International Handbook of Creativity. Edward Elgar Publishing, Cheltenham, UK.

[65]Keys R, 1981. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process, 29(6):1153-1160.

[66]Kim J, Lee JK, Lee KM, 2016. Accurate image super-resolution using very deep convolutional networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1646-1654.

[67]Kingma DP, Welling M, 2013. Auto-encoding variational Bayes. https://arxiv.org/abs/1312.6114

[68]Kong S, Shen XH, Lin Z, et al., 2016. Photo aesthetics ranking network with attributes and content adaptation. Proc 14th European Conf on Computer Vision, p.662-679.

[69]Krizhevsky A, Hinton G, 2009. Learning Multiple Layers of Features from Tiny Images. Technical Report, University of Toronto, Toronto.

[70]Kwak H, An J, Jansen BJ, 2017. Automatic generation of personas using YouTube social media data. Proc 50th Hawaii Int Conf on System Sciences, p.833-842.

[71]Larsen ABL, Sonderby SK, Larochelle H, et al., 2016. Autoencoding beyond pixels using a learned similarity metric. Proc 33rd Int Conf on Machine Learning, p.1558-1566.

[72]LeCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278-2323.

[73]Ledig C, Theis L, Huszør F, et al., 2017. Photo-realistic single image super-resolution using a generative adversarial network. IEEE Conf on Computer Vision and Pattern Recognition, p.105-114.

[74]Li C, Wand M, 2016. Precomputed real-time texture synthesis with Markovian generative adversarial networks. Proc 14th European Conf on Computer Vision, p.702-716.

[75]Li CC, Chen T, 2009. Aesthetic visual quality assessment of paintings. IEEE J Sel Top Signal Process, 3(2):236-252.

[76]Li HH, Wang JG, Tang MM, et al., 2017. Polarization-dependent effects of an Airy beam due to the spin-orbit coupling. J Opt Soc Am A, 34(7):1114-1118.

[77]Li XT, Liu SF, Kautz J, et al., 2019. Learning linear transformations for fast arbitrary style transfer. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3809-3817.

[78]Li YJ, Fang C, Yang JM, et al., 2017. Universal style transfer via feature transforms. Proc 31st Conf on Neural Information Processing Systems, p.386-396.

[79]Liu GL, Reda FA, Shih KJ, et al., 2018. Image inpainting for irregular holes using partial convolutions. Proc 15th European Conf on Computer Vision, p.85-105.

[80]Liu H, Singh P, 2004. ConceptNet—apractical commonsense reasoning tool-kit. BT Technol J, 22(4):211-226.

[81]Liu MY, Huang X, Mallya A, et al., 2019. Few-shot unsupervised image-to-image translation. https://arxiv.org/abs/1905.01723

[82]Liu ZW, Luo P, Wang XG, et al., 2015. Deep learning face attributes in the wild. Proc IEEE Int Conf on Computer Vision, p.3730-3738.

[83]Lowdermilk T, 2013. User-Centered Design: a Developer‘s Guide to Building User-Friendly Applications. O‘Reilly, Beijing, China.

[84]Lu X, Lin Z, Shen XH, et al., 2015. Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. Proc IEEE Int Conf on Computer Vision, p.990-998.

[85]Luo YW, Tang XO, 2008. Photo and video quality evaluation: focusing on the subject. Proc 10th European Conf on Computer Vision, p.386-399.

[86]Ma S, Liu J, Chen WC, 2017. A-lamp: adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment. Proc 30th IEEE Conf on Computer Vision and Pattern Recognition, p.722-731.

[87]Maguire M, Bevan N, 2002. User requirements analysis. In: Hammond J, Gross T, Wesson J (Eds.), Usability: Gaining a Competitive Edge. Springer, Boston, USA, p.133-148.

[88]Mai L, Jin HL, Liu F, 2016. Composition-preserving deep photo aesthetics assessment. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.497-506.

[89]Matthews T, Judge T, Whittaker S, 2012. How do designers and user experience professionals actually perceive and use personas? Proc Conf on Human Factors in Computing Systems, p.1219-1228.

[90]McCaffrey T, Krishnamurty S, 2015. The obscure features hypothesis in design innovation. Int J Des Creat Innov, 3(1):1-28.

[91]McGinn J, Kotamraju N, 2008. Data-driven persona development. Proc Conf on Human Factors in Computing Systems, p.1521-1524.

[92]Miaskiewicz T, Kozar KA, 2011. {Personas and user-centered design: how can personas benefit product design processes?} Des Stud, 32(5):417-430.

[93]Mikolov T, Chen K, Corrado G, et al., 2013. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781

[94]Miller GA, 1995. Wordnet: a lexical database for English. Commun ACM, 38(11):39-41.

[95]Mirza M, Osindero S, 2014. Conditional generative adversarial nets. https://arxiv.org/abs/1411.1784

[96]Miyato T, Kataoka T, Koyama M, et al., 2018. Spectral normalization for generative adversarial networks. Int Conf on Learning Representations.

[97]Murray N, Marchesotti L, Perronnin F, 2012. AVA: a large-scale database for aesthetic visual analysis. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2408-2415.

[98]Nazeri K, Ng E, Joseph T, et al., 2019. Edgeconnect: generative image inpainting with adversarial edge learning. https://arxiv.org/abs/1901.00212

[99]Nelson BA, Wilson JO, Rosen D, et al., 2009. Refined metrics for measuring ideation effectiveness. Des Stud, 30(6):737-743.

[100]Nielsen L, Hansen KS, Stage J, et al., 2015. {A template for design personas: analysis of 47 persona descriptions from Danish industries and organizations}. Int J Sociotechnol Knowl Dev, 7(1):45-61.

[101]Niles I, Pease A, 2001. Towards a standard upper ontology. Proc Int Conf on Formal Ontology in Information Systems, p.2-9.

[102]Nilsback ME, Zisserman A, 2008. Automated flower classification over a large number of classes. Proc 6th Indian Conf on Computer Vision, Graphics & Image Processing, p.722-729.

[103]Odena A, Olah C, Shlens J, 2017. Conditional image synthesis with auxiliary classifier GANs. Proc 34th Int Conf on Machine Learning, p.4043-4055.

[104]Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1-2.

[105]Park T, Liu MY, Wang TC, et al., 2019. Semantic image synthesis with spatially-adaptive normalization. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2337-2346.

[106]Pathak D, Krähenbühl P, Donahue J, et al., 2016. Context encoders: feature learning by inpainting. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2536-2544.

[107]Peeters JR, Verhaegen PA, Vandevenne D, et al., 2010. Refined metrics for measuring novelty in ideation. IDMME Virtual Concept Research in Interaction Design, Article 4.

[108]Perera D, Zimmermann R, 2019. {CNGAN: generative adversarial networks for cross-network user preference generation for non-overlapped users}. World Wide Web Conf, p.3144-3150.

[109]Pruitt J, Adlin T, 2005. The Persona Lifecycle: Keeping People in Mind Throughout Product Design. Elsevier, Amsterdam, p.724.

[110]Radford A, Metz L, Chintala S, 2016. {Unsupervised representation learning with deep convolutional generative adversarial networks. Proc 4th Int Conf on Learning Representations.}

[111]Reed SE, Akata Z, Yan XC, et al., 2016a. Generative adversarial text to image synthesis. Proc 33rd Int Conf on Machine Learning, p.1681-1690.

[112]Reed SE, Akata Z, Mohan S, et al., 2016b. Learning what and where to draw. Advances in Neural Information Processing Systems, p.217-225.

[113]Rigau J, Feixas M, Sbert M, 2008. Informational aesthetics measures. IEEE Comput Graph Appl, 28(2):24-34.

[114]Russell SJ, Norvig P, 2016. Artificial Intelligence: a Modern Approach. Pearson Education Limited, Harlow, Essex.

[115]Saleh B, Elgammal A, 2015. Large-scale classification of fine-art paintings: learning the right metric on the right feature. https://arxiv.org/abs/1505.00855

[116]Salimans T, Goodfellow IJ, Zaremba W, et al., 2016. Improved techniques for training GANs. Advances in Neural Information Processing Systems, p.2226-2234.

[117]Salminen J, Sengün S, Kwak H, et al., 2017. Generating cultural personas from social data: a perspective of middle eastern users. Proc 5th Int Conf on Future Internet of Things and Cloud Workshops, p.120-125.

[118]Salminen J, Jansen BJ, An J, et al., 2018a. Are personas done? Evaluating their usefulness in the age of digital analytics. Persona Stud, 4(2):47-65.

[119]Salminen J, Jung SG, An J, et al., 2018b. Findings of a user study of automatically generated personas. Proc Conf on Human Factors in Computing Systems, p.LBW097.

[120]Salminen J, Engün S, Jung SG, et al., 2019. Design issues in automatically generated persona profiles: a qualitative analysis from 38 think-aloud transcripts. Proc Conf on Human Information Interaction and Retrieval, p.225-229.

[121]Schwarz K, Wieschollek P, Lensch HPA, 2018. Will people like your image? Learning the aesthetic space. Proc IEEE Winter Conf on Applications of Computer Vision, p.2048-2057.

[122]Shah JJ, Kulkarni SV, Vargas-Hernandez N, 2000. Evaluation of idea generation methods for conceptual design: effectiveness metrics and design of experiments. J Mech Des, 122(4):377-384.

[123]Simonyan K, Zisserman A, 2014. Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556

[124]Strohmann T, Siemon D, Robra-Bissantz S, 2017. brAInstorm: intelligent assistance in group idea generation. Proc 12thInt Conf on Design Science Research in Information System and Technology, p.457-461.

[125]Strothotte T, Schlechtweg S, 2002. Non-photorealistic Computer Graphics: Modeling, Rendering, and Animation. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

[126]Tang X, Wang ZW, Luo WX, et al., 2018. Face aging with identity-preserved conditional generative adversarial networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.7939-7947.

[127]Tang XO, Luo W, Wang XG, 2013. Content-based photo quality assessment. IEEE Trans Multim, 15(8):1930-1943.

[128]Vandevenne D, Verhaegen PA, Dewulf S, et al., 2015. A scalable approach for ideation in biologically inspired design. Artif Intell Eng Des Anal Manuf, 29(1):19-31.

[129]Varshney LR, Pinel F, Varshney KR, et al., 2019. A big data approach to computational creativity: the curious case of Chef Watson. IBM J Res Dev, 63(1):7:1-7:18.

[130]Verma P, Smith JO, 2018. Neural style transfer for audio spectograms. https://arxiv.org/abs/1801.01589

[131]Wang J, Yu LT, Zhang WN, et al., 2017. IRGAN: a minimax game for unifying generative and discriminative information retrieval models. Proc 40>th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.515-524.

[132]Wang TC, Liu MY, Zhu JY, et al., 2018. Video-to-video synthesis. https://arxiv.org/abs/1808.06601

[133]Wang WG, Shen JB, 2017. {Deep cropping via attention box prediction and aesthetics assessment}. Proc IEEE Int Conf on Computer Vision, p.2205-2213.

[134]Wang WN, Cai D, Wang L, et al., 2016. Synthesized computational aesthetic evaluation of photos. Neurocomputing, 172:244-252.

[135]Wang WS, Yang S, Zhang WS, et al., 2018. Neural aesthetic image reviewer. https://arxiv.org/abs/1802.10240

[136]Wang XT, Yu K, Wu SX, et al., 2018. ESRGAN: enhanced super-resolution generative adversarial networks. European Conf on Computer Vision, p.63-79.

[137]Wu JJ, Zhang CK, Xue TF, et al., 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Advances in Neural Information Processing Systems, p.82-90.

[138]Xu T, Zhang PC, Huang QY, et al., 2018. AttnGAN: fine-grained text to image generation with attentional generative adversarial networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1316-1324.

[139]Yan Y, Wang JR, Tang C, et al., 2019. Research on the development of contemporary design intelligence driven by neural network technology. In: Marcus A, Wang WT (Eds.), Design, User Experience, and Usability. Design Philosophy and Theory. Springer, Cham, p.368-381.

[140]Yang HY, Huang D, Wang YH, et al., 2018. Learning face age progression: a pyramid architecture of GANs. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.31-39.

[141]Yang WM, Zhang XC, Tian YP, et al., 2019. Deep learning for single image super-resolution: a brief review. IEEE Trans Multim, 21(12):3106-3121.

[142]Yang Y, Zhuang YT, Wu F, et al., 2008. {Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval}. IEEE Trans Multim, 10(3):437-446.

[143]Yi ZL, Zhang H, Tan P, et al., 2017. DualGAN: unsupervised dual learning for image-to-image translation. Proc IEEE Int Conf on Computer Vision, p.2868-2876.

[144]Yoon Y, Jeon HG, Yoo D, et al., 2015. Learning a deep convolutional network for light-field image super-resolution. Proc IEEE Int Conf on Computer Vision, p.57-65.

[145]You S, You N, Pan MX, 2019. PI-REC: progressive image reconstruction network with edge and color domain. https://arxiv.org/abs/1903.10146

[146]Yu F, Zhang YD, Song SR, et al., 2015. LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. https://arxiv.org/abs/1506.03365

[147]Yu JH, Lin Z, Yang JM, et al., 2018a. Free-form image inpainting with gated convolution. https://arxiv.org/abs/1806.03589

[148]Yu JH, Lin Z, Yang JM, et al., 2018b. Generative image inpainting with contextual attention. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.5505-5514.

[149]Zakharov E, Shysheya A, Burkov E, et al., 2019. Few-shot adversarial learning of realistic neural talking head models. https://arxiv.org/abs/1905.08233

[150]Zeiler MD, Taylor GW, Fergus R, 2011. Adaptive deconvolutional networks for mid and high level feature learning. Proc IEEE Int Conf on Computer Vision, p.2018-2025.

[151]Zhang H, Xu T, Li H, et al., 2017. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. Proc IEEE Int Conf on Computer Vision, p.5907-5915.

[152]Zhang H, Xu T, Li H, et al., 2019. StackGAN++: realistic image synthesis with stacked generative adversarial networks. IEEE Trans Patt Anal Mach Intell, 41(8):1947-1962.

[153]Zhang JJ, Yu JH, Zhang K, et al., 2017. Computational aesthetic evaluation of logos. ACM Trans Appl Perc, 14(3), Article 20.

[154]Zhang R, Isola P, Efros AA, 2016. Colorful image colorization. Proc 14th European Conf on Computer Vision, p.649-666.

[155]Zhao H, Gallo O, Frosio I, et al., 2016. Loss functions for image restoration with neural networks. IEEE Trans Comput Imag, 3(1):47-57.

[156]Zhu JY, Park T, Isola P, et al., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf on Computer Vision, p.2242-2251.

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