生成式影像合成

The primary goal of this course is to provide students with a solid understanding of the fundamental concepts of Generative Artificial Intelligence, develop their practical implementation skills, and enable them to apply these techniques flexibly across different domains. The course also guides students to explore the application of Generative AI in image synthesis, cultivating their ability to implement and experiment with Generative AI methods. Through this course, students will gain deeper insight into the current limitations and challenges of Generative AI and examine future developments in image generation. This course also invites Dr. Chia-Min Cheng, a senior manager from the AI technology division at MediaTek, to co-teach the class. He will share the latest industry insights and discuss real-world challenges and business opportunities related to Generative AI in Computer Vision and Machine Learning, particularly in the transition from research to commercial products. Upon completion of the course, students will develop the following core competencies: ․ Fundamental knowledge and technical skills in Generative AI and image generation. ․ Awareness of emerging technologies and the ability to analyze new trends. ․ The ability to conduct independent research and development, along with teamwork and project management skills. ․ Innovative thinking and problem-solving abilities, enabling students to apply their knowledge to promote technological innovation and societal progress.

Deep Learning, Python

無備註

․ Lectures on Theory and Principles: The instructor will provide in-depth explanations of the theoretical foundations and key principles of Generative Artificial Intelligence. ․ Hands-on Practice and Case Studies: Emphasis will be placed on practical exercises and real-world case analysis to strengthen students’ ability to apply the concepts learned in class. ․ Student Presentations and Discussions: Students are encouraged to actively participate in presentations and discussions to facilitate peer learning and academic exchange. Teaching assistants will also be available to support students in understanding and applying course materials.

․ Assignments (30%): Includes programming assignments and literature review reports designed to evaluate students’ ability to apply theoretical knowledge and develop practical implementation skills. ․ Midterm Report (30%): Students are required to select a research paper related to Generative AI and image generation published within the past three years and prepare a research report. Additional credit will be awarded if the report includes a working demo or technical implementation. ․ Final Project (40%): Students will independently choose a topic related to Generative AI and image generation and complete both a research report and an implementation project. Evaluation criteria include clarity of problem formulation, innovation and practicality of the proposed solution, and the completeness and performance of the technical implementation.

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地點

Room 374, Engineering Building VI

時間

Mondays, 11:00–12:00

聯絡方式

yfyangd@gmail.com