Final Project

15 minute read

As part of this course, students must complete a small research project instead of a final exam.

We provide you with a pool of project ideas mostly based on recent papers and our current research interests. The projects will be supervised by course TAs or other graduate students from the VISTA lab. During the project period you will be able to meet with course TAs for guidance during dedicated office hours. The project can be performed in groups of at most two students, and at most two groups can work on the same project topic (separately).

Some of the projects are “reimplementation projects” where you should start from a specific paper, implement it, suggest at least one improvement and evaluate it using experiments which you must also devise and implement. Selecting this kind of project means we may expect more of you, in terms of the project ideas and experimental methodology.

For graduate students only, we allow you to define a custom project that relates to your own research topic.

If you have further questions regarding the course final project, please contact Chaim Baskin.

The project topics spreadsheet link will be sent by email to all registered students.

Project procedure

  1. Each group should read the topics and paper abstracts in the project topics spreadsheet.
  2. Each group must fill out the registration form with their top-3 priorities or custom project proposal until date 01/02/2020.
  3. Soon after you submit your priorities, we will approve one of them. Submitting the form sooner will increase your chance of getting your first priority.
  4. You will start working on your projects. You can schedule periodic meetings with course staff at dedicated office hours which will be published.
  5. Your submission should be a detailed report. It should explain the problem and the paper you implemented (if relevant), explain your specific enhancements and modifications, showcase all your results (both reproduction of the paper and novel results, if any), compare your solution to the baseline method (if relevant), etc. See below for details submission instructions.
  6. The submission date for the project is 16/04/2020. Note that the course servers will not be available to Winter 19-20 students after this date.

Please view the project topics spreadsheet (link will be sent via emali) and then fill out the registration form with your priorities.

Initial office hours will be given by Chaim on Monday 26/01, 10:00 at the VISTA lab, Taub 120. You can use these hours to consult regarding project selection.

We will publish additional office hours in the coming weeks.

Report structure and evaluation

The following list details what your project report should contain and its impact on the grade.

  1. Abstract (10%). Summarize your work. Briefly introduce the problem, the methods and state the key results.

  2. Intro (25%). Review the papers relevant to your project. Explain the problem domain, existing approaches and the specific contribution of the relevant paper(s). Also detail the drawbacks which you plan to address. If it’s a custom project, explain your specific motivation and goals. Cite any other work as needed.

  3. Methods (25%). If implementing an existing paper, explain the original approach as well as your ideas for modifications, additions or improvements to the algorithm/task/domain etc., as relevant. Otherwise, provide a detailed explanation of your approach. In both cases, explain the empirical and/or theoretical motivation for what you are doing. Finally, describe the data you will be using for evaluation.

  4. Implementation and experiments (20%). Describe the experiments performed and their configurations, what was compared to what and the evaluation metrics used and why. Explain all implementation details such as model architectures used, data preprocessing/augmentation approaches, loss formulations, training methods and hyperparameter values.

    Note: You can use existing code, e.g. in your implementation but specify what you used and which parts you implemented yourself.

  5. Results (20%). Present all results in an orderly table and include graphs or figures as you see fit. Discuss, analyze and explain your results. Compare to previous works and other approaches for your task.


Create a zip file titled (replace id1/id2 with your IDs) and email it to Chaim and Aviv.

The zip file should include:

  1. A single PDF document, report.pdf, containing your project report. It must be structured according to the sections listed above.
  2. A folder src/ containing all your code.
  3. A README file (plain text/markdown) explaining:
    1. The structure of the code in the src/ folder: What is implemented in each package/module.
    2. Steps to reproduce your results using this code: Where to get and place the data, how to run all the data processing steps, how to run training and evaluation.