一般觀眾對於「類神經網路」之藝術風格轉移認知模式先期研究

近年隨著人工智慧所引領的類神經網路深度學習技術日趨成熟，而其相關應用已經逐漸蔓延至包括藝術在內的各個領域，對藝術作品的創作、體驗、審美和欣賞將帶來新的機遇和挑戰。目前該領域多注重演算法的精進，對於演算法的精進主要牽涉到兩個問題：1.就科技（理性）而言，觀眾能否辨認經由電腦的藝術風格轉移？2.就藝術（感性）而言，哪些主要因素會影響經由電腦的藝術風格轉移？因此，本研究透過認知人因工程研究，分析人們如何感知創作者的編碼過程（色彩、筆觸、紋理）與閱聽者的解碼過程（技術、語意、效果）之影響，以期能對於構建人工智慧應用於藝術創作之研究具有積極的助益。本研究係屬一系列的相關研究，先期研究招募了31位藝術、美學與設計等專家學者參與，對野獸派畫作與對應轉換圖的契合度和認知效果進行評估。結果發現，不同繪畫的風格特性是可以被認知與分辨，風格轉移的整體效果會影響觀眾的喜好度；最後，提出一個探討藝術風格轉移的研究模式。除了有助於藝術風格轉移演算法的改進外，也為人工智慧應用於藝術創作相關研究提供參考。

關鍵字

認知人因工程；人工智慧；類神經網路；藝術風格轉移

並列摘要

In recent years, the use of AI is becoming more mature with the development of neural network technology, and access to various fields including art creation. Meanwhile, it brings new opportunities and challenges in the field of invention, experience, aesthetics and appreciation of art. At present, this field only focuses on the optimization of algorithms, but there are two key points in the improvement of algorithms. (1) As far as technology (rationality) is concerned, can the audience recognize the artistic style transfer through computers? (2) In terms of Art (sensibility), what are the main factors that affect the artistic style transfer through computers? Therefore, the purpose of this paper is to analyze the difference of audience's cognition during the creator's encoding process (color, stroke, texture) and the audience's decoding process (technical level, semantic level, effectiveness level). This study is a series of related studies, in which 31 experts and scholars with a background in art, aesthetics and/or design, were recruited to participate in the previous study to evaluate the degree of fitness and the effect of cognitive between fauvism portraits and corresponding converted images. The results showed that the stylistic characteristics of different images can be recognized and distinguished by subjects. The overall effect of the style transfer will affect the audience's preferences. At last, a research model of artistic style transfer is put forward. The results of the research not only provide suggestions for the optimization of the NST algorithm but also provide some references for the research on the application of AI into art creation.

並列關鍵字

Cognitive Ergonomics ； Artificial Intelligent ； Neural Network ； Artistic Style Transfer

參考文獻

林榮泰（2007）。蒙家就是我家－從蒙得裡安談文化創意的簡約設計。藝術欣賞，3（5），4-9。doi:10.29479/AA.200710.0001 Lin, R. (2007). Mondrian’s house is my house- Discussing clean design in cultural innovation from Mondrian’s point of view. Art Appreciation, 3(5), 4-9. doi:10.29479/AA.200710.0001 [in Chinese, semantic translation]

汪曼穎、葉怡玉、黃榮村（2013）。臺灣認知心理學的應用：從認知研究到科技脈絡裡的人性化設計。中華心理學刊，55（3），381-404。doi:10.6129/CJP.20121118Wang, M. Y., Yeh, Y. Y., & Huang, J. T. (2013). The applications of cognitive psychology in Taiwan-from cognitive research to human-centered design in the context of emerging technology. Chinese Journal of Psychology, 55(3), 381-404. doi:10.6129/CJP.20121118 [in Chinese, semantic translation]

Augustin, M. D., Defranceschi, B., Fuchs, H. K., Carbon, C., & Hutzler, F. (2011). The neural time course of art perception: An ERP study on the processing of style versus content in art. Neuropsychologia, 49(7), 2071-2081. doi:10.1016/j.neuropsychologia.2011.03.038

Bar, Y., Levy, N., & Wolf, L. (2015). Classification of artistic styles using binarized features derived from a deep neural network. In L. Agapito, M. M. Bronstein & C. Rother (Eds.), Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol. 8925 (pp. 71-84). Cham: Springer. https://doi.org/10.1007/978-3-319-16178-5_5

Bengio, Y. (2009). Learning deep architectures for AI. Found Trends Mach Learn, 2(1), 1-127. doi:10.1561/2200000006

國際替代計量

一般觀眾對於「類神經網路」之藝術風格轉移認知模式先期研究

全文下載

主題瀏覽