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Deep Learning for Computer Vision 
111 - 1  /  Fall  2022

News

2022/09/08: We've taken addition ~100 students, and the registration code has been sent out to those who are admitted. If you are not added in and would like to audit the online course, please send the request to TA (with your NTU email address).

2022/09/03: For those who'd like to register the course, we will announce the registration policy at the 1st lecture on Sept. 6th.

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 details of deep learning architectures, with a particular focus on understanding and designing learnable models for solving various vision tasks.

Goals

​This course will expose students to cutting-edge research — starting from a refresher in basics of machine learning, computer vision, neural networks, to recent developments. 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
09/06
Course logistics & registration; Machine Learning 101
week 1-1
week 1-2
 
2
09/13
Introduction to Convolutional Neural Networks (I)
​​week 2
 
3
09/20
Introduction to Convolutional Neural Networks (II)
​​Tutorials on Python, Github, etc. (by TAs)
week 3
HW #1 out 
4
09/27
Object Detection & Segmentation; Generative Model
week 4
 
5
10/04
Generative Adversarial Networks, and Diffusion Model
week 5
 
6
10/11
​Transfer Learning for Visual Classification & Synthesis
week 6
HW #1 due & HW #2 out 
7
10/18
​Guest Lecture (TBD)
 
ICIP week
8
10/25
Recurrent Neural Networks​
week 8
 
9
11/01
Transformer;  Vision & Language (I)
week 9
HW #2 due & HW #3 out 
10
11/08
Vision & Language (II); Few-Shot Learning (I)
week 10
CVPR due
11
11/15
N/A
 
No class (校慶)
12
11/22
3D Vision
week 12
HW #3 due & HW #4 out
13
11/29
Announcement of Final Project
 
NeurIPS week
& Final Projects out
14
12/06
Self-Supervised Learning & Guest Lecture
week 14
 
15
12/13
Federated Learning, Domain Generalization and More Advanced Topics
week 15
HW #4 due
17
12/29 Thur
Presentation for Final Projects
 
 

Contacts

Prof. Yu-Chiang Frank Wang:   ycwang@ntu.edu.tw
TA mail:                                     ntudlcv@gmail.com

Teaching Assistants

Picture
Yu-Hsuan Chen
MK-514
TA Hours: ​Mon. 12:20 - 13:10
圖片
Yuan-Yi Hsu
MK-514
TA Hours:​ Mon. 12:20 - 13:10
Picture
Gi-Luen Huang
BL-530
​TA Hours: Wed. 12:20 - 13:10
圖片
圖片
圖片
Zi-Ting Chou
MK-514
TA Hours: Wed. 13:20 - 14:10
Kai-Po Chang
MK-514
​TA Hours: ​Thu. 14:20 - 15:10
I-Jieh Liu
MK-514
TA Hours: ​Wed. 13:20 - 14:10
圖片
圖片
圖片
Chi-Pin Huang
MK-514
TA Hours: Thu. 14:20 - 15:10
Jr-Jen Chen
MK-514
TA Hours: Tue. 12:20 - 13:10
Fu-Cheng Pan
MK-514
TA Hours: Fri. 12:20 - 13:10
圖片
圖片
Yu-Chien Liao
MK-514
TA Hours: Fri. 12:20 - 13:10
Yang-Che Tseng
MK-514
TA Hours: Tue. 12:20 - 13:10


​學新館 514 室 (MK 514) @ NTU

Contact Us
Tel: 02-33663241
​Admin. Assistant: Fang-Ru Shih (frs106 AT ntu.edu.tw)
Web Admin. : Yen-Cheng Liu (ycliu93 AT ntu.edu.tw)
Web Photo Credit: Yuan-Fang Lin (b02901003 AT ntu.edu.tw)


​

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