Group Project Overview
Today's advanced Large Language Model (LLM) technologies, such as OpenAI o3, Google Gemini-2.5 Pro, and DeepSeek-R1, have revolutionized the way we learn, and are best suited for "learning by doing" — a hands-on experience that prioritizes practical experiments. The method is superior to the traditional theory-first approach. At the heart of this approach is the importance of working on a meaningful project—one that sparks passion, excites curiosity, and motivates us to dive in and see it through to completion.
Project Structure
Students must form teams of five members by Week 3 and select a team leader. The team will independently take on a Deep Learning (DL) project that both excites and challenges them, with minimal supervision. The team leader will be responsible for submitting all group materials, including the member list, project proposal, and final report. This structure encourages collaboration, initiative, and ownership of the learning process.
Project Types
⭐ Create Your Own Method on an existing dataset
Taking an existing dataset and adapting an existing method to make it your own DL method. You modify parameters, work with existing neural networks, apply what you've learned in the lecture, and aim to make them more efficient.
⭐⭐ Beat the Classics - Implement a DL method and compare to non-DL baseline
Challenge the state-of-the-art using algorithms without DL or with DL algorithms that aim to surpass this baseline. Compare the results and demonstrate the ability to outperform the baseline, referred to as "beat".
⭐⭐⭐ Beat the Stars - Improve the state of the art
By selecting a research paper, the goal is to demonstrate how to outperform the current state-of-the-art papers. It's important to note that due to the rapid pace of paper publication, it might be difficult to stay up-to-date with the latest advancements. Nonetheless, the objective is to select a recent paper as a baseline and attempt to beat it.
Group Project Assessment Timeline
Project Team Formation (Week 3)
- Create a team of 5 people and choose a team leader
- Team leader is responsible for submitting all group project materials
- Send team member list and elected leader to: eelmpo@cityu.edu.hk
Project Proposal (Week 4)
A 5-page project proposal (not including references) submitted in PDF format to CANVAS.
Required Content:- Project Title
- Student Name, Student ID and Email Address of each member
- Summary with goals (300 words)
- Problem statement and objectives
- Methodology and dataset description
- Baseline selection and experimental setup
- Timeline and collaboration plan
- References
Oral Presentation (Week 13)
10-minute PowerPoint presentation to the entire class assessing communication skills and technical understanding.
Presentation must include:- Short description of project and objectives
- Explanation of implemented algorithm and relevant theory
- Demonstration of working program with results
- Upscaling Images with Neural Networks by Geoffrey Litt
- This is a great technical presentation for students to learn about industry presentation styles.
- YOLO Presentation at CVPR2016
Final Technical Report and Deliverables (Week 14)
Minimum 25-page final report following technical report structure.
Required Submissions:- Final technical report (PDF) - 25+ pages minimum
- PowerPoint presentation file
- Python source code
- 3-4 minute demo video summary
- Appendix A: Individual Contributions Report
Template Technical Report: Use this technical report template with LaTex. Must include Appendix A for individual contributions assessment.
Project Hints and Resources
Key Principles
- Your passion is your compass - Choose topics that excite you
- Quality standard - Aim for publishable quality work
- AI tools - Use Elicit, SciSpace, Consensus, NotebookLM for research
- Academic resources - Use Google Scholar for literature review
- Dataset considerations - Plan data collection/preprocessing time carefully
Presentation Schedule
Section Week 12A (Nov 17, 2026)
Siyuan Wu (Team Leader), Wenxiao Chen, Xueyi Luo, Jinjing Li, Songyang Li
Group 2:
YU Zhen (Team Leader),
Zhai Fanshun,
Xie Honghao,
Luo Juyuan,
Liu Pei
Group 3:
Chunxi Li(Team Leader),
Lishuoguang Yu,
Wenlong Li,
Jingtong Ma,
Hongle Wu
Group 4:
Zhou Dianqi (Team Leader),
Chen Siyuan,
Chu Ka Po,
Dai Lixuan,
Wang Jiawei
Group 5:
LIU WEI (Team Leader),
MA Dingcheng,
LI Zilin,
ZHENG Canjie,
LU Xinchi
Group 6:
Jialiang Jiang (Team Leader),
Can Liu,
Weixin Wang,
Gaozhan Kang,
Wenlan Ma
Group 7:
Han Yilin (Team Leader),
Pan Siyu,
Lan Murong,
Li Zhaokun,
Cheng Lijun
Group 8:
PENG Dingyuan (Team Leader),
ZHAI Yuxuan,
XU Yifeng,
LU Chenxi,
YANG Ruiming
Group 9:
Bocheng Lin (Team Leader),
Qiushi Gu,
Yuanman Liu,
Maoshen Li,
Zeyi He
Section Week 12B (Nov 17, 2026)
Group 10:DUAN Zehao (Team Leader), CHENG Fenglei, LI Yuzhang, LIU Zixuan, SONG Yurui
Group 11:
Qizhen Tang (Team Leader),
NUERMAIMAITI Adilai,
Jing li,
Ziheng Lu,
Xuyang Huang
Group 12:
YANG Jiayi (Team Leader),
YAO Liyun,
HAN Qiuchi,
ZHANG Yuelin,
LIN Hanwen
Group 13:
LI Xingyao (Team Leader),
HE Weiyu,
LI Xiaojing,
WANG Xuefei,
REN Yujie
Group 14:
Liu Guanlong (Team Leader),
Zhang Huiyang,
Peng Yusu,
Liu Yuheng,
Guo Fuzheng
Group 15:
ZHAO Pengbo (Team Leader),
BAI Keyi,
WANG Jingru,
PAN Yangchen,
GUO Huangang
Group 16:
Ge Yuting (Team Leader),
Guo Wenjie,
Liang Yongkang,
Yan Yincheng,
Tian Qixin
Group 17:
XU Ziao (Team Leader),
LIU Ziang,
Wang Zihan,
Xue Yangdian,
Mao Chenyang
Group 18:
Shi Jiarui (Team Leader),
Shi Chenxu, Chen Boyong, Shang Depei, Li Yuanjing
Section Week 13A (Nov 24, 2026)
Group 19:Liu Jinyuan (Team Leader), Li Muxiao, Huang Chenming, Jiang Weiyi, Zhao Mingchuan
Group 20:
LI Huanrong (Team Leader),
PANG Bo,
ZHANG Huanyu,
CHEN Junxu,
Zhang Yangqi
Group 21:
Group 22:
Group 23:
Group 24:
Group 25:
Group 26:
Group 27:
Section Week 13B (Nov 24, 2026)
Group 28:
Group 29:
Group 30:
Group 31:
Group 32:
Group 33:
Group 34:
Group 35:
Group 36:
Department of Electrical Engineering, City University of Hong Kong
