Training Machines to See and Evaluate Scenic Beauty

Overview

Explore how deep learning and computer vision can be trained to recognize and evaluate scenic beauty, challenging traditional notions of aesthetic appreciation being uniquely human.

Syllabus

• Introduction to Aesthetic Evaluation
Overview of Scenic Beauty and Aesthetic Concepts
Historical Perspective on Visual Aesthetics
• Basics of Computer Vision
Introduction to Image Processing
Key Algorithms and Techniques in Computer Vision
Overview of Neural Networks in Computer Vision
• Deep Learning Foundations
Introduction to Deep Learning Models
Convolutional Neural Networks (CNNs)
Transfer Learning and Pre-trained Models
• Datasets and Data Preparation
Curating Datasets for Scenic Beauty Evaluation
Data Augmentation Techniques
Labeling and Annotating Scenic Images
• Training Models for Aesthetic Evaluation
Defining Features of Scenic Beauty
Training and Fine-Tuning CNNs
Handling Bias and Ensuring Diversity in Training Data
• Evaluation Metrics for Scenic Beauty
Designing Qualitative and Quantitative Evaluation Metrics
Implementing A/B Testing and Comparative Studies
• Advanced Topics in Machine Aesthetics
Generative Adversarial Networks (GANs) for Image Generation
Understanding Style Transfer and Its Applications
Exploring Multimodal Approaches Integrating Text and Image
• Ethical Considerations and Human-AI Collaboration
Ethical Implications of AI-Driven Aesthetic Evaluation
Bridging Human Aesthetic Judgment and Machine Evaluation
Applications and Future Directions in AI-Driven Scenic Evaluation
• Case Studies and Applications
Real-world Applications in Tourism, Film, and Art
Reviewing Case Studies of AI Evaluators in Action
• Final Project
Proposal and Implementation of an AI Model for Scenic Evaluation
Presentation and Critique of Project Outcomes

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Subjects

Data Science