This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus ...

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https://www.riss.kr/link?id=A107342694
2021
-
KCI등재
학술저널
18-32(15쪽)
1
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus ...
This article deals with the changes triggered by artificial intelligence related to image production. The image originally started as a “manual image” drawn by human hands and then differentiated into a “technical image” produced by apparatus in the 19th century. Since then, with the development of image generation algorithms, the automaticity of image production has become more advanced. This generative model, represented by GAN, produces images only by 'calculation' based on probability and statistics. Currently, GAN is receiving the most attention in the field of computer vision, and it excels in various tasks such as creating high-resolution images, translating images, and compositing photos. GAN learns the original photos and creates an image similar to it, which goes beyond the limitations of apparatus such as human hands and cameras. This 'computational image' belongs to the category of 'technical image' in a broad sense, but differs from photography, the first technical image in that there is no indication object. In addition, the automaticity of the program, the nature of the apparatus, is accelerating the exclusion of humans from the image production process in an extreme form. As a result, humans cannot reflect their intentions in image production and are in a position to become a simple consumer of images.
참고문헌 (Reference)
1 Flusser, Vilem, "Vilem(1985). Into the universe of technical image" University of Minnesota Press 2011
2 Radford, Alec, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"
3 Flusser, Vilem, "Towards a philosophy of photography" Reaktion Books Ltd 1983
4 Jetchev, Nikolay, "Texture Synthesis with Spatial Generative Adversarial Networks"
5 Choi, Yunjey, "StarGAN:Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation"
6 Zhang, Han, "Self-Attention Generative Adversarial Networks" 2019
7 Pan, Zhaoqing, "Recent Progress on Generative Adversarial Networks (GANs): a survey" 7 : 36322-36333, 2019
8 Karras, Tero, "Progressive Growing of GANs for improved quality, stability, and variation"
9 Li, Chuan, "Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks"
10 Ledig, Christian, "Photo-Ralistic single image Super-Resolution using a Generative Adversarial Network"
1 Flusser, Vilem, "Vilem(1985). Into the universe of technical image" University of Minnesota Press 2011
2 Radford, Alec, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"
3 Flusser, Vilem, "Towards a philosophy of photography" Reaktion Books Ltd 1983
4 Jetchev, Nikolay, "Texture Synthesis with Spatial Generative Adversarial Networks"
5 Choi, Yunjey, "StarGAN:Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation"
6 Zhang, Han, "Self-Attention Generative Adversarial Networks" 2019
7 Pan, Zhaoqing, "Recent Progress on Generative Adversarial Networks (GANs): a survey" 7 : 36322-36333, 2019
8 Karras, Tero, "Progressive Growing of GANs for improved quality, stability, and variation"
9 Li, Chuan, "Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks"
10 Ledig, Christian, "Photo-Ralistic single image Super-Resolution using a Generative Adversarial Network"
11 Goodfellow, Ian, "NIPS 2016Tutorial: Generative Adversarial Networks"
12 Kim, Taeksoo, "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
13 Bergmann, Urs, "Learning Texture Manifolds with the Periodic Spatial GAN"
14 Brock, Andrew, "Large Scale GAN Training for Hige Fidelity Natural Image Synthesis"
15 Salimans, Tim, "Improved Techniques for Training GANs"
16 Isola, Phillip, "Image-to-Image Translation with Conditional Adversarial Networks"
17 Goodfellow, Ian, "Generative Adversarial Nets"
18 Pyungjong Park, "From program to posgram : The Fundamental Problems of Photography and Artificial Intelligence Art" Society of Contemporary Art Science 22 (22): 107-130, 2018
19 Mirza, Mehdi, "Conditional Generative Adversarial Nets"
20 Indolia, Sakshi, "Conceptual Understanding of Convolutional Neural Network – A Deep Learning Approach" 2018 : 679-688, 2018
21 Huang, Rui, "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis" 2439-2448, 2017
22 O’Shea, Keiron, "An Introduction to Convolutional Neural Networks"
23 Besanon, "Alain. L’Image interdite: une histoire intellectuelle de l’iconoclasme" Fayard 1994
인물사진에서의 시간인식과 인물간의 교감에 관하여 - Thirty-Minute Dialog, One-Hour Portrait 시리즈를 중심으로 -
수직 시각에서 도시 건축 사진의 시지각 법칙 분석 - 홍콩 건축 사진을 중심으로 -
자연적 물성을 이미지로 재현하는 사진적 연구 - 사진연작 <낮고, 빠르게 쏘기>를 중심으로 -
학술지 이력
| 연월일 | 이력구분 | 이력상세 | 등재구분 |
|---|---|---|---|
| 2023 | 평가 | 재인증평가 신청대상 (재인증) | |
| 2020-01-01 | 등재 | 등재학술지 선정 (재인증) | ![]() |
| 2018-01-01 | 등재 | 등재후보학술지 선정 (신규평가) | ![]() |
| 2016-12-01 | 등재 | 등재후보 탈락 (계속평가) | |
| 2015-12-01 | 등재 | 등재후보로 하락 (기타) | ![]() |
| 2011-01-01 | 등재 | 등재학술지 유지 (등재유지) | ![]() |
| 2009-01-01 | 등재 | 등재학술지 유지 (등재유지) | ![]() |
| 2007-01-01 | 등재 | 등재 1차 FAIL (등재유지) | ![]() |
| 2004-01-01 | 등재 | 등재학술지 선정 (등재후보2차) | ![]() |
| 2003-01-01 | 등재 | 등재후보 1차 PASS (등재후보1차) | ![]() |
| 2002-01-01 | 등재 | 등재후보 1차 FAIL (등재후보1차) | ![]() |
| 2000-07-01 | 등재 | 등재후보학술지 선정 (신규평가) | ![]() |
[제18회 김옥길기념강좌] 인공지능, 감정, 휴머니즘(Human-Compatible Artificial Intelligence’)’
이화여자대학교 스튜어드 러셀누구나 할 수 있는 데이터 분석과 인공지능[Data Analysis and Artificial Intelligence for Everyone]
K-MOOC 인하공업전문대학 이세훈누구나 할 수 있는 데이터 분석과 인공지능[Data Analysis and Artificial Intelligence for Everyone]
K-MOOC 인하공업전문대학 이세훈딥러닝심화: CNN부터 GAN까지
K-MOOC 한국연구재단 산업교육센터 김영훈인공지능의 이해
건국대학교 백우진