Course overview

This course offers a comprehensive exploration of machine learning techniques for visual data (e.g., images or videos) synthesis and editing. The course will cover a range of topics from classical algorithms (e.g., image filtering) and deep learning methods (e.g., fundamental deep learning models including CNNs and Transformers, as well as deep generative models including VAEs, GANs, and Diffusion Models). This course consists of instructor-led lectures and student-led presentations. Participants will learn to implement image synthesis algorithms, to understand cutting-edge image synthesis techniques, and to explore intriguing research questions. This course will be of particular interest to students seeking to delve into fields of computer vision, machine learning, and AIGC.

Prerequisites

This course requires students to have basic knowledge in machine learning, Python programming, and computer vision (recommended but not necessary).

Assignments

1. Three homework assignments during the first half of the course

2. Three paper reviews during the second half of the course

3. Presenting once in class (as a group of 2 people)

4. A final project (as a group of 1-2 people).

Grading

1. Three homework assignments (18% = 6%*3)

• VAE

• DCGAN 

• Diffusion-based model

2. Three paper reviews (12% = 4%*3)

3. Paper presentation (20%)

4. Final project (40%)

5. Class participation (10%)

*Students will be allowed a total of five late days per course. Each additional late day will incur a 10% penalty.