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Lif31up's Blog
  • Welcome! I'm Myeonghwan
  • How to Read the Pages
  • Fundamental Machine Learning
    • Foundational Work of ML: Linear/Logistic Regression
    • Early-stage of AI: Perceptron and ADALINE
    • What is Deep Learning?: Artificial Neural Network to Deep Neural Network
    • Challenges in Training Deep Neural Network and the Latest Solutions
  • Modern AI Systems: An In-depth Guide to Cutting-edge Technologies and Applications
  • Few Shot Learning
    • Overview on Meta Learning
    • Prototypical Networks for Few-shot Learning
    • Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
  • Natural Language Process
    • * Tokenization and Stemming, Lemmatization, Stop-word Removal: Core Fundamental of NLP
    • Bag-of-Words
  • Front-end Development
    • Overview on Front-end Development
    • Learning React Basic
      • React Component: How They are Rendered and Behave in Browser
      • State and Context: A Key Function to Operate the React Application
      • Design Pattern for Higher React Programming
  • Songwriting
    • A Comprehensive Guide to Creating Memorable Melodies through Motif and Phrasing
  • Sound Engineering
    • How to Install and Load Virtual Studio Instruments
    • A Guide to Audio Signal Chains and Gain Staging
    • Equalizer and Audible Frequency: How to Adjust Tone of the Signal
    • Dynamic Range: the Right Way to Compress your Sample
    • Acoustic Space Perception and Digital Reverberation: A Comprehensive Analysis of Sound Field Simulat
  • Musical Artistry
    • What is Artistry: in Perspective of Modern Pop/Indie Artists
    • Visualizing as Musical Context: Choose Your Aesthetic
    • Analysis on Modern Personal Myth and How to Create Your Own
    • Instagram Management: Business Approach to Your Social Account
  • Art Historiography
    • Importance of Art Historiography: Ugly and Beauty Across Time and Space
    • Post-internet Art, New Aesthetic and Post-Digital Art
    • Brutalism and Brutalist Architecture
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  1. Art Historiography

Importance of Art Historiography: Ugly and Beauty Across Time and Space

λ―Έμˆ μ‚¬(art historiography)λŠ” 역사, 방법둠, λ―Έμˆ μ‚¬ 이둠을 μ—°κ΅¬ν•˜λŠ” ν•™λ¬Έμž…λ‹ˆλ‹€. μ΄λŠ” κ°œλ³„ μž‘ν’ˆ λΆ„μ„λ³΄λ‹€λŠ” 미술이 μ—­μ‚¬μ μœΌλ‘œ μ–΄λ–»κ²Œ μ„€λͺ…λ˜κ³  ν‰κ°€λ˜μ–΄ μ™”λŠ”μ§€, 그리고 μ‹œκ°„μ˜ 흐름에 따라 κΈ°κ΄€, ν•™μžλ“€μ˜ 연ꡬ와 역사·문화적 λ§₯락을 톡해 μ–΄λ–»κ²Œ ν˜•μ„±λ˜μ–΄ μ™”λŠ”μ§€λ₯Ό νƒκ΅¬ν•©λ‹ˆλ‹€. λ˜ν•œ λ―Έμˆ μ‚¬μ˜ λ°œμ „ κ³Όμ •μ—μ„œ λ‚˜νƒ€λ‚œ 비평적 관점, νŒ¨λŸ¬λ‹€μž„, 편견, ν•΄μ„μ˜ λ³€ν™” 등을 μ—°κ΅¬ν•©λ‹ˆλ‹€.

  • λ―Έμˆ μ‚¬μ˜ 역사(history of art history): λ¬Έλͺ…μ˜ μ΄ˆκΈ°λΆ€ν„° λ―Έμˆ μ‚¬κ°€ μ–΄λ–»κ²Œ 기술되고 λ°œμ „ν•΄ μ™”λŠ”μ§€λ₯Ό μ²΄κ³„μ μœΌλ‘œ μ—°κ΅¬ν•˜λŠ” λΆ„μ•Όμž…λ‹ˆλ‹€.

  • 극볡방법과 이둠적 μ ‘κ·Ό(methodologies/theoretical approaches): λ―Έμˆ μ‚¬λ₯Ό ν•΄μ„ν•˜κΈ° μœ„ν•œ λ‹€μ–‘ν•œ 극볡방법둠과 이둠적 틀을 μ—°κ΅¬ν•˜λŠ” λΆ„μ•Όμž…λ‹ˆλ‹€.

    • ν˜•μ‹μ£Όμ˜(formalism): μž‘ν’ˆμ˜ ꡬ성과 μŠ€νƒ€μΌμ„ μ€‘μ‹¬μœΌλ‘œ λ―Έμˆ μ„ ν‰κ°€ν•˜λŠ” 전톡적 μ ‘κ·Όλ²•μž…λ‹ˆλ‹€.

    • 도상학/도상해석학(iconography/iconology): μž‘ν’ˆμ΄ μ§€λ‹Œ 상징적 의미λ₯Ό λΆ„μ„ν•˜κ³  ν•΄μ„ν•˜λŠ” λ°©λ²•λ‘ μž…λ‹ˆλ‹€.

    • μ‚¬νšŒλ¬Έν™”μ  μ ‘κ·Ό(social art approach): 마λ₯΄ν¬μ‹œμ¦˜, νŽ˜λ―Έλ‹ˆμ¦˜, νƒˆμ‹λ―Όμ£Όμ˜, κΈ€λ‘œλ²Œλ¦¬μ¦˜ λ“± λ‹€μ–‘ν•œ μ‚¬νšŒμ  κ΄€μ μ—μ„œ λ―Έμˆ μ„ λΆ„μ„ν•˜λŠ” λ°©λ²•λ‘ μž…λ‹ˆλ‹€.

  • 비평과 μž¬λΉ„μ „ν™”(critiques and revision): 전톡적인 예술 μ„€λͺ…학에 λŒ€ν•œ 도전과 μž¬ν•΄μ„μ„ μΆ”κ΅¬ν•©λ‹ˆλ‹€. κ΅­κ°€μ£Όμ˜λ‚˜ μ‹λ―Όμ£Όμ˜μ™€ 같은 μ΄λ°μ˜¬λ‘œκΈ°κ°€ λ―Έμˆ μ‚¬ μ„œμˆ κ³Ό μž‘ν’ˆ 해석에 미친 영ν–₯을 μ—°κ΅¬ν•©λ‹ˆλ‹€.

  • μƒν˜Έν•™μ œμ  μ ‘κ·Ό(interdisciplinary connection): λ―Έμˆ μ‚¬λ₯Ό μ² ν•™, μ‚¬νšŒν•™, 기술 λ“± λ‹€μ–‘ν•œ ν•™λ¬Έ 뢄야와 μ—°κ³„ν•˜μ—¬ λΆ„μ„ν•˜κ³  ν•΄μ„ν•˜λŠ” 방법을 νƒκ΅¬ν•©λ‹ˆλ‹€.

미술 μ—°κ΅¬μ˜ μ€‘μš”μ„±

  • λ―Έμˆ μ‚¬μ  μ„œμˆ μ— λ‚΄μž¬λœ νŽΈκ²¬μ„ μΈμ‹ν•˜κ²Œ ν•©λ‹ˆλ‹€.

  • μ„œκ΅¬ 쀑심적 관점을 λ„˜μ–΄ λ‹€μ–‘ν•œ μ‹œκ°μ„ 탐ꡬ할 수 있게 ν•©λ‹ˆλ‹€.

  • λ―Έμˆ μ‚¬μ˜ 이해 κ³Όμ •μ—μ„œ μž‘μš©ν•˜λŠ” μ •μΉ˜μ , μ‚¬νšŒμ  인과관계λ₯Ό νŒŒμ•…ν•  수 있게 ν•©λ‹ˆλ‹€.

  • 각각의 연ꡬ 방법둠이 μ§€λ‹Œ μž₯단점을 ν‰κ°€ν•˜μ—¬ λ”μš± 포괄적인 μ‹œκ°μ„ κ°–μΆœ 수 있게 ν•©λ‹ˆλ‹€.

λ””μ§€ν„Έ μ•„νŠΈ νžˆμŠ€ν† λ¦¬λž€?

DAH(Digital Art History)λŠ” 예술 μž‘ν’ˆκ³Ό μ˜ˆμˆ μ‚¬μ  자료λ₯Ό λ””μ§€ν„Έ 기술둜 ν•΄μ„ν•˜λŠ” μ ‘κ·Ό λ°©μ‹μž…λ‹ˆλ‹€. μ΄λŠ” 비ꡐ적 μ΅œκ·Όμ— λ“±μž₯ν–ˆμœΌλ©°, 컴퓨터λ₯Ό ν™œμš©ν•œ μ‹œκ°ν™”μ™€ 뢄석을 μˆ˜ν–‰ν•©λ‹ˆλ‹€.

  • Large-Scale Pattern Recognition: λŒ€κ·œλͺ¨ μž‘ν’ˆ λ°μ΄ν„°μ—μ„œ μ•Œκ³ λ¦¬μ¦˜μ„ ν™œμš©ν•˜μ—¬ μŠ€νƒ€μΌ, 색채 ꡬ성, ꡬ성적 μš”μ†Œμ˜ νŒ¨ν„΄κ³Ό κ²½ν–₯을 λΆ„μ„ν•©λ‹ˆλ‹€.

  • 신경망 뢄석(network analysis): μ˜ˆμˆ κ°€, ν›„μ›μž, κΈ°κ΄€λ“€ κ°„μ˜ 잠재적 μ—°κ²° 관계λ₯Ό νŒŒμ•…ν•˜λŠ” 데 ν™œμš©λ©λ‹ˆλ‹€.

  • 이미지 인식/λ¨Έμ‹  λŸ¬λ‹(image recognition and machine learning): 인곡지λŠ₯을 ν™œμš©ν•˜μ—¬ 예술 μž‘ν’ˆμ˜ νŠΉμ§•μ„ λΆ„μ„ν•˜κ³  μΆ”μΆœν•©λ‹ˆλ‹€.

  • 3D μž¬κ΅¬μ„±μ£Όμ˜μ™€ 가상 ν˜„μ‹€ μ˜ˆμˆ μ‚¬(3D reconstruction/virtual art history): 3차원 λ””μ§€ν„Έ μž¬κ΅¬μ„±κ³Ό 가상 ν˜„μ‹€ κΈ°μˆ μ„ ν™œμš©ν•˜μ—¬ 예술 μž‘ν’ˆκ³Ό 역사적 μž₯μ†Œλ₯Ό λ³΅μ›ν•˜κ³  μ—°κ΅¬ν•˜λŠ” 신생 λΆ„μ•Όμž…λ‹ˆλ‹€.

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