9/18 update: We have already notified all the students who have been selected and are eligible for enrollment. Please check the email you filled in the registration form.
For NTU students who would like to take this course but are not registered yet, please complete and submit the registration form https://forms.gle/EaDTf4oG8aNZGZDX7 between 9/14 Mon 9 a.m. and 9/15 Tue 23:59 Taiwan time. We will notify the registration results by email no later than 9/18 5 p.m.
Due to space limit, NO sit-in is allowed.
For NTU students who would like to take this course but are not registered yet, please complete and submit the registration form https://forms.gle/EaDTf4oG8aNZGZDX7 between 9/14 Mon 9 a.m. and 9/15 Tue 23:59 Taiwan time. We will notify the registration results by email no later than 9/18 5 p.m.
Due to space limit, NO sit-in is allowed.
Introduction
Computer Vision has become ubiquitous in our society, with applications in image/video search and understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, segmentation, localization and detection. Recent developments in neural network (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 details of the deep learning architectures with a focus on learning end-to-end models for solving these 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. The emphasis will be on student-led paper presentations and discussions. Each topic will begin with instructor lectures to present context and background material.
Syllabus
Week |
Date |
Topic |
Course Materials |
Remarks |
1 |
09/15 |
Course Logistics |
|
|
2 |
09/22 |
Machine Learning 101 |
||
3 |
09/29 |
Intro to Neural Networks; Convolutional Neural Network (I) |
HW #1 out |
|
4 |
10/06 |
Convolutional Neural Network (II): Visualization & Extensions of CNN |
||
5 |
10/13 |
Tutorials on Python, Github, etc. (by TAs) |
HW #1 due |
|
6 |
10/20 |
Visualization of CNN (II); Object Detection & Segmentation |
HW #2 out |
|
7 |
10/27 |
Image Segmentation; Generative Models |
||
8 |
11/03 |
Generative Adversarial Network (GAN) |
[] |
|
9 |
11/10 |
Transfer Learning for Visual Classification & Synthesis; Representation Disentanglement |
HW #2 due; HW #3 out |
|
10 |
11/17 |
Guest Lectures (Dr. Trista Chen & David Chou) |
||
11 |
11/24 |
Representation Disentanglement; Recurrent Neural Networks & Transformer (I) |
||
12 |
12/01 |
Recurrent Neural Networks & Transformer (I) Meta-Learning; Few-Shot and Zero-Shot Classification (I) |
HW #3 due |
|
13 |
12/08 |
Meta-Learning; Few-Shot and Zero-Shot Classification (II) |
HW #4 out |
|
14 |
12/15 |
From Domain Adaptation to Domain Generalization |
Team-up for Final Projects |
|
15 |
12/22 |
Beyond 2D vision (3D and Depth) |
||
16 |
12/29 |
Image Inpainting and Outpainting; Guest Lecture |
HW #4 due |
|
17 |
1/05 |
Guest Lectures |
[] |
|
1/18-22 |
Presentation for Final Projects |
TBD |
Contacts
Teaching Assistants
Cheng-Fu Yang
BL-527 TA Hours: Fri. 11:00~12:00 |
Cheng-Yo (Ugo) Tan
BL-527 TA Hours: Wed. 16:00~17:00 |
Chiao-An Yang
BL-527 TA Hours: Wed. 14:00~15:00 |