DLCV - VISION & LEARNING LAB

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.

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	DLCV_W0
2	09/22	Machine Learning 101	DLCV_W1
3	09/29	Intro to Neural Networks; Convolutional Neural Network (I)	DLCV_W2	HW #1 out
4	10/06	Convolutional Neural Network (II): Visualization & Extensions of CNN	DLCV_W3
5	10/13	Tutorials on Python, Github, etc. (by TAs)	DLCV_W4	HW #1 due
6	10/20	Visualization of CNN (II); Object Detection & Segmentation	DLCV_W5	HW #2 out
7	10/27	Image Segmentation; Generative Models	DLCV_W6
8	11/03	Generative Adversarial Network (GAN)	DLCV_W7	[]
9	11/10	Transfer Learning for Visual Classification & Synthesis; Representation Disentanglement	DLCV_W8	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)	DLCV_W10
12	12/01	Recurrent Neural Networks & Transformer (I) Meta-Learning; Few-Shot and Zero-Shot Classification (I)	DLCV_W11	HW #3 due
13	12/08	Meta-Learning; Few-Shot and Zero-Shot Classification (II)	DLCV_W12	HW #4 out
14	12/15	From Domain Adaptation to Domain Generalization	DLCV_W13	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