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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | Shih-Yu Chen | en |
| dc.contributor.author | 陳思伃 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:28:45Z | - |
| dc.date.available | 2022-08-19 | |
| dc.date.available | 2022-11-24T03:28:45Z | - |
| dc.date.copyright | 2021-11-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-24 | |
| dc.identifier.citation | Atarsaikhan, G., Iwana, B. K., Uchida, S. (2018, 2018-04-01). Contained Neural Style Transfer for Decorated Logo Generation. Paper presented at the 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). Balmer, J. M. (1998). Corporate identity and the advent of corporate marketing. Journal of marketing management, 14(8), 963-996. Balmer, J. M. T. (2001). Corporate identity, corporate branding and corporate marketing ‐ Seeing through the fog. European Journal of Marketing, 35(3/4), 248-291. doi:10.1108/03090560110694763 Bottomley, P. A., Doyle, J. R. (2006). The interactive effects of colors and products on perceptions of brand logo appropriateness. Marketing Theory, 6(1), 63-83. doi:10.1177/1470593106061263 Cian, L., Krishna, A., Elder, R. S. (2014). This Logo Moves Me: Dynamic Imagery from Static Images. Journal of Marketing Research, 51(2), 184-197. doi:10.1509/jmr.13.0023 Foroudi, P., Melewar, T. C., Gupta, S. (2014). Linking corporate logo, corporate image, and reputation: An examination of consumer perceptions in the financial setting. Journal of Business Research, 67(11), 2269-2281. doi:10.1016/j.jbusres.2014.06.015 Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 2672-2680. Henderson, P. W., Giese, J. L., Cote, J. A. (2004). Impression Management using Typeface Design. Journal of Marketing, 68(4), 60-72. doi:10.1509/jmkg.68.4.60.42736 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. Paper presented at the Advances in neural information processing systems. Jiang, Y., Gorn, G. J., Galli, M., Chattopadhyay, A. (2016). Does Your Company Have the Right Logo? How and Why Circular- and Angular-Logo Shapes Influence Brand Attribute Judgments. Journal of Consumer Research, 42(5), 709-726. doi:10.1093/jcr/ucv049 Karras, T., Aila, T., Laine, S., Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196. Karras, T., Aila, T., Laine, S., Lehtinen, J. (2018). Progressive Growing of GANs for Improved Quality, Stability, and Variation. Karras, T., Laine, S., Aila, T. (2019). A style-based generator architecture for generative adversarial networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. Luffarelli, J., Stamatogiannakis, A., Yang, H. (2019). The Visual Asymmetry Effect: An Interplay of Logo Design and Brand Personality on Brand Equity. Journal of Marketing Research, 56(1), 89-103. doi:10.1177/0022243718820548 Madden, T. J., Hewett, K., Roth, M. S. (2000). Managing Images in Different Cultures: A Cross-National Study of Color Meanings and Preferences. Journal of International Marketing, 8(4), 90-107. doi:10.1509/jimk.8.4.90.19795 Mino, A., Spanakis, G. (2018, 2018-12-01). LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on Color. Paper presented at the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Mirza, M., Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. Oeldorf, C., Spanakis, G. (2019, 2019-12-01). LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks. Paper presented at the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Pittard, N., Ewing, M., Jevons, C. (2007). Aesthetic theory and logo design: examining consumer response to proportion across cultures. International Marketing Review, 24(4), 457-473. doi:10.1108/02651330710761026 Van Den Bosch, A. L. M., Elving, W. J. L., De Jong, M. D. T. (2006). The impact of organisational characteristics on corporate visual identity. European Journal of Marketing, 40(7/8), 870-885. doi:10.1108/03090560610670034 Van Der Lans, R., Cote, J. A., Cole, C. A., Leong, S. M., Smidts, A., Henderson, P. W., . . . Schmitt, B. H. (2009). Cross-National Logo Evaluation Analysis: An Individual-Level Approach. Marketing Science, 28(5), 968-985. doi:10.1287/mksc.1080.0462 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81064 | - |
| dc.description.abstract | 用人工智慧自動生成商標可以節省設計商標時的時間與金錢，並提供設計師與企業主管商標設計時的靈感。先前研究使用生成對抗網路自動生成商標，並使用定量指標衡量模型的表現。我們的研究除了用定量指標衡量模型表現，也希望用商標設計元素與品牌態度分析生成之商標，從一個管理的觀點衡量生成對抗網路模型之表現。我們使用兩種近期熱門的生成對抗網路之變異模型生成商標，比較兩者模型的表現，同時也與真實的商標做比較。我們選圖形特徵與生成對抗網路相似之真實商標進行比較。此研究結果顯示，兩種生成對抗網路生成商標之品牌態度也有顯著差異。另外，研究結果也顯示生成對抗網路與真實商標之品牌態度（尤其美觀與整體好感）有顯著差異，因此生成對抗網路所生成之商標較適合當作草稿，較不適合直接使用。不過也因為生成對抗網路所生成之商標較抽象，因此適合應用在各領域與產業的企業。此研究是第一個使用管理的觀點衡量生成對抗網路生成之商標。而我們的研究結果可以提供未來生成對抗網路應用與改進之方向，並提供設計師與企業主管關於生成對抗網路商標之參考。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:28:45Z (GMT). No. of bitstreams: 1 U0001-2108202123443300.pdf: 3918251 bytes, checksum: cf05b911d6f50d894f86ce66f5b4b1c5 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Objectives 2 Chapter 2 Literature Review 4 2.1 Brand Logo 4 2.1.1 Logo Design 4 2.2 Logo Generation 6 2.2.1 Generative Adversarial Networks (GANs) 6 2.2.2 Conditional Generations 7 2.2.3 Style-Based Generations 8 2.2.4 Conditional StyleGAN 11 2.2.5 MSG-StyleGAN (Multi-Scale Gradients StyleGAN) 13 2.2.6 Fréchet Inception Distance 15 Chapter 3 Research Testbed 17 3.1 Research Testbed 17 3.1.1 Clearbit-WRDS Dataset 17 3.1.2 Clustering Method 18 Chapter 4 Research Design 21 4.1 Research Questions 21 4.2 Logo Generation and Evaluation Design 23 Chapter 5 Experimental Results 26 5.1 Quantitative Evaluation Results 26 5.2 Brand Evaluation Results 27 Chapter 6 Conclusions and Future Directions 48 6.1 Conclusion 48 REFERENCE 50 Appendix A 53 | |
| dc.language.iso | en | |
| dc.subject | 品牌商標 | zh_TW |
| dc.subject | 商標設計 | zh_TW |
| dc.subject | 商標評量 | zh_TW |
| dc.subject | 品牌認知 | zh_TW |
| dc.subject | 對抗網路自動生成商標 | zh_TW |
| dc.subject | Brand Logo | en |
| dc.subject | GAN-generated Logos | en |
| dc.subject | Brand Perception | en |
| dc.subject | Logo Evaluation | en |
| dc.subject | Logo Design | en |
| dc.title | 探討使用生成對抗網路自動生成商標之品牌態度 | zh_TW |
| dc.title | Evaluating Brand Perception on Logo Synthesized by Generative Adversarial Network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡宇泰(Hsin-Tsai Liu),曹承礎(Chih-Yang Tseng) | |
| dc.subject.keyword | 品牌商標,商標設計,商標評量,品牌認知,對抗網路自動生成商標, | zh_TW |
| dc.subject.keyword | Brand Logo,Logo Design,Logo Evaluation,Brand Perception,GAN-generated Logos, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU202102575 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-25 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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