DLCV - VISION & LEARNING LAB

News

2024/09/08: [Reminder: Classroom change] We now change to BL-113 to accommodate a total of 150 students, starting Sept. 10th.
2024/09/06: [Course registration update] The permission code for course registration has been sent out. If you do not plan to take the course, please let the instructor know ASAP so that we can have more people in.
2024/08/08: Please refer to the policy in Week 1 (W1-1) slides if you are NTU students and would like to register this course.

Introduction

This course focuses on the transformative power of deep learning based approaches in computer vision. Students will learn fundamental knowledge and explore state-of-the-art techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), Transformer, Diffusion Models and 3D vision, with a strong emphasis on model designs and their practical applications. Through rigorous hands-on projects and in-depth theoretical discussions, you will gain a comprehensive understanding of how deep learning models can be designed, trained, and optimized for complex visual tasks. This course prepares you for advanced research and professional roles in the ever-evolving field of computer vision.

Goals

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.

Syllabus

Week	Date	Topic	Course Materials	Remarks
1	09/03	Course Logistics & Registration; Intro to Neural Nets	W1-1 W1-2
2	09/10	Convolutional Neural Networks & Image Segmentation	W2	HW #1 out
3	09/17	No class		Mid-Autumn Festival
4	09/24	Generative Models (I) - AE, VAE, & Diffusion Model (I)	W4	HW #1 due
5	10/01	Guest Lecture: Dr. Jun-Cheng Chen, Academia Sinica		ECCV week
6	10/8	Generative Models (II) - Diffusion Model (II), GAN	W6	HW # 2 out
7	10/15	Recurrent Neural Networks & Transformer	W7
8	10/22	Transformer; Vision & Language Models	W8
9	10/29	Vision & Language Models; Multi-Modal Learning	W9	HW #2 due; HW #3 out
10	11/05	Parameter-Efficient Finetuning; Unlearning, Debiasing, and Interoperability	W10
11	11/12	Guest Lecture: Linda Huang, Senior Dir., GeValyn Associates Chenchi Kuo, Senior Dir., Marvell Technology
12	11/19	3D Vision	W12	HW #3 due; HW #4 out
13	11/26	Object Detection	W13	Final Project Announcement
14	12/03	Guest Lecture: Prof. Ming-Ching Chang, SUNY, Albany, USA Guest Lecture: Dr. Trista Chen, Director, Microsoft AI Research Center, Taiwan		HW #4 due
15	12/10	Zero-Shot vs. Continual Learning with Foundation Models; Progress Check for Final Projects & General Q&A
17	12/26 Thu	Final Project Presentation