COMPSCI 682 Neural Networks: A Modern Introduction

Acknowlegements

These project guidelines originally accompany the Stanford CS class CS231n, and are now provided here for the UMass class COMPSCI 682 with minor changes reflecting our course contents. Many thanks to Fei-Fei Li and Andrej Karpathy for graciously letting us use their course materials!

Important Dates (tentative)

Course project proposal due: 10/3
Course project milestone due: 10/31
Final course project write-up due: 12/2
Project presentations: 12/03 - 12/10

Overview

The course project is an opportunity for you to apply what you have learned in class to a problem of your interest.

Your are encouraged to select a topic and work on your own project. Potential projects usually fall into these two tracks:

Here you can find some sample project ideas:

To inspire ideas, you might look at recent deep learning publications from top-tier vision conferences, as well as other resources below.

For applications, this type of projects would involve careful data preparation, an appropriate loss function, details of training and cross-validation and good test set evaluations and model comparisons. Don't be afraid to think outside of the box. Some successful examples can be found below:

Deep neural networks also run in real time on mobile phones and Raspberry Pi's - feel free to go the embedded way. You may find this TensorFlow demo on Android helpful.

For models, deep neural networks have been successfully used in a variety of computer vision and NLP tasks. This type of projects would involve understanding the state-of-the-art vision or NLP models, and building new models or improving existing models. The list below presents some papers on recent advances of deep neural networks in the computer vision community.

We also provide a list of popular computer vision datasets:

Grading Policy

  Final Project: 50% of final grade
  - Proposal: 5% of final grade
  - Milestone: 15% of final grade
  - Final Report: 25% of final grade
     write-up (20% of Final Report):
      •  clarity, structure, language, references
      •  background literature survey, good understanding of the problem
      •  good insights and discussions of methodology, analysis, results, etc.
     technical (30% of Final Report):
      •  correctness
      •  depth
      •  innovation
     evaluation and results (30% of Final Report):
      •  sound evaluation metric
      •  thoroughness in analysis and experimentation
      •  results and performance
  - Project Presentation: 5% of final grade
  

Project Proposal

The project proposal should be concise (~400 words). You can use the provided template. Your proposal should contain:

Submission: Please upload a PDF file to Gradescope. Please coordinate with your teammate and submit only under ONE of your accounts, and add your teammate on Gradescope.

Project Milestone

Your project milestone report should be between 2 - 3 pages using the template (pdf, latex source). The following is a suggested structure for your report:

Submission: Please upload a PDF file to Gradescope. Please coordinate with your teammates and submit only under ONE of your accounts, and add your teammates on Gradescope.

Final Submission

Your final write-up should be between 6 - 8 pages using the same template as the milestone report (pdf, latex source) and fully flesh out the sections in the milestone. After the class, we may post all the final reports online so that you can read about each others' work. If you do not want your writeup to be posted online, then please let us know at least a week in advance of the final writeup submission deadline.

Project Presentation

All teams will present their work during class in the last three lectures. The order will be randomized and you are expected to be present in all of the three days. Each team will get 2-3 minutes (depending on the number of projects) to present thier work. This is similar to a spotlight presentation at conferences such as CVPR and NeurIPS. Note, the remote participants will be asked to pre-record and upload a video of their presentation.

More details on the presentation format will be provided later in the semester.

Collaboration Policy

You can work in teams of 2 or 3 people. Individual projects or larger group sizes are permitted only in exceptional circumstances (e.g., remote students, etc) and need prior permission from the instructor. Similar, group projects larger than three We do expect that projects done with 3 people have more impressive writeup and results than 2 person projects.

Honor Code

You may consult any papers, books, online references, or publicly available implementations for ideas and code that you may want to incorporate into your strategy or algorithm, so long as you clearly cite your sources in your code and your writeup. However, under no circumstances may you look at another group’s code or incorporate their code into your project.