深度學習於電腦視覺 Deep Learning for Computer Vision
The syllabuses on both this page and the NTU online course information are synchronized.
Course Information
| Item | Content |
| Course title | Deep Learning for Computer Vision |
| Semester | 113-1 |
| Designated for |
Intelligent Medicine Program GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING |
| Instructor | YU-CHIANG WANG |
| Curriculum No. | CommE 5052 |
| Curriculum Id No. | 942 U0660 |
| Class | |
| Credit | 3 |
| Full/Half Yr. | Half |
| Required/Elective | Elective |
| Time | Tuesday 2,3,4(9:10~12:10) |
| Place | 博理113 |
| Remarks |
Course Syllabus
| Item | Content |
| Course Description | Please check the latest info and announcements at https://vllab.ee.ntu.edu.tw/dlcv.html. |
| Course Objective | This course will expose students to cutting-edge research — starting from fundamentals of deep learning to its recent advances in various vision applications. You will be expected to master key concepts, such as neural network architectures and their designs, training techniques, and performance optimization strategies. As a major part of this course, final projects are offered to foster critical thinking and problem-solving skills, preparing you to contribute to cutting-edge research or tackle real-world challenges in the real-world problems. Active participation, completion of hands-on projects, and engagement in theoretical discussions will be crucial to your success in this course. |
| Course Requirement | |
| Student Workload (expected study time outside of class per week) | |
| References | |
| Designated Reading |
Progress
| Week | Date | Topic |
| Week 1 | 09/03 | Course Logistics & Registration; Intro to Neural Nets |
| Week 2 | 09/10 | Convolutional Neural Networks & Image Segmentation |
| Week 3 | 09/17 | No Class |
| Week 4 | 09/24 | Generative Models (I) - AE, VAE & GAN |
| Week 5 | 10/01 | Guest Lecture |
| Week 6 | 10/08 | Generative Models (II) - Diffusion Model |
| Week 7 | 10/15 | Recurrent Neural Networks & Transformer |
| Week 8 | 10/22 | Vision & Language Models |
| Week 9 | 10/29 | Unlearning, Debiasing, and Interoperability |
| Week 10 | 11/05 | Multi-Modal Learning |
| Week 11 | 11/12 | Parameter-Efficient Finetuning; Efficient Deep Learning |
| Week 12 | 11/19 | 3D Vision |
| Week 13 | 11/26 | Transfer & Adversarial Learning |
| Week 14 | 12/03 | Federated Learning |
| Week 15 | 12/10 | Progress Check for Final Projects |
| Week 17 | 12/26 Thu | Final Project Presentation (1:30pm-5pm) |
Grading
| NO | Item | Pc | Explanations for the conditions |
Adjustment methods for students
| Adjustment method | |
| Teaching methods | |
| Assignment submission methods | |
| Exam methods | |
| Others |
Office Hour
| Remarks | None |