News
2023/12/05: No class on Dec. 12th due to NeurIPS 2023. Good luck with all you finals and see you at the final presentation!
Introduction
Computer vision has become ubiquitous in our society, with a variety of applications in image/video search and understanding, medicine, drones, and self-driving cars. As the core to many of the above applications, visual analysis such as image classification, segmentation, localization and detection would be among the well-known problems in computer vision. Recent developments in neural networks (a.k.a. deep learning) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the deep learning models, with particular focuses on understanding and designing such models for solving various computer vision tasks.
Goals
This course will expose students to cutting-edge research — starting from fundamentals of deep learning to its recent advances such as generative AI models. Each topic will begin with instructor lectures to present context and background material, followed by discussions and homework assignments, allowing the students to develop hand-on experiences on deep learning techniques for solving practical computer vision problems.
Syllabus
Week |
Date |
Topic |
Course Materials |
Remarks |
1 |
09/05 |
Course Logistics & Registration; Intro to Neural Nets |
||
2 |
09/12 |
Convolutional Neural Networks & Training Techniques |
||
3 |
09/19 |
Extensions of CNN & Self-Supervised Learning; Image Segmentation |
HW #1 out |
|
4 |
09/26 |
Generative Models (I) - AE, VAE & GAN |
||
5 |
10/03 |
Guest Lecture (Dr. Jun-Cheng Chen, Academia Sinica) Lecture Title: An Overview of Adversarial Attack and Defense with its Application to Object Detection and Deepfake |
ICCV week |
|
6 |
10/10 |
No Class |
Double Tenth Day; |
|
7 |
10/17 |
Generative Models (II) - GAN & Diffusion Model; Transfer Learning |
HW #1 due & HW #2 out |
|
8 |
10/24 |
Recurrent Neural Networks & Transformer |
||
9 |
10/31 |
Vision Transformer & Large Language Models |
||
10 |
11/07 |
Vision & Language Models; Parameter-Efficient Finetuning |
HW #2 due & HW #3 out |
|
11 |
11/14 |
Multimodal Learning; Object Detection |
||
12 |
11/21 |
Guest Lecture |
||
13 |
11/28 |
3D Vision |
HW #3 due & HW #4 out Final Projects out |
|
14 |
12/05 |
Federated Learning & Advanced Topics |
||
15 |
12/12 |
No class |
NeurIPS week HW #4 due |
|
17 |
12/28 Thur |
Final Project Presentation |
Contacts
Teaching Assistants
I-Jieh Liu
MK-514 TA Hours: Thu. 13.20 ~ 14.10 |
Zi-Ting Chou
MK-514 TA Hours: Thu. 13.20 ~ 14.10 |
Bin-Shih Wu
MK-514 TA Hours: Wed. 13.20 ~ 14.10 |
Jr-Jen Chen
MK-514 TA Hours: Fri. 11.20 ~ 12.10 |
Wei-Yuan Cheng
MK-514 TA Hours: Wed. 13.20 ~ 14.10 |
Yu-Chien Liao
MK-514 TA Hours: Fri. 11.20 ~ 12.10 |
Hsueh-Han Yang
MK-514 TA Hours: Mon. 13.20 ~ 14.10 |
Hsi-Che Lin
MK-514 TA Hours: Mon. 13.20 ~ 14.10 |