The course project is an opportunity for you to apply what you have learned in class to a problem of your interest. You can consider any kind of project which has to be solved via any deep learning method that we have seen. We recommend you to tackle the problem from a very applicative perspective where you should focus on building and training a deep neural network from scratch.
Some general potential problems could be the following:
- Computer vision applications (classification, object detection, segmentation)
- Natural language processing applications (machine translation, image captioning, chatbot, recommendation systems)
- Conditional generative models (image synthesis, style-transfer, drug-discovery)
- Deep reinforcement learning (video games, robotics)
... some more inspiration:
- Stanford CS231n Project instructions (Spring 2018)
- Stanford CS231n Project reports (Spring 2017)
- Stanford CS231n Project reports (Winter 2016)
- Stanford CS231n Project reports (Winter 2015)
- Stanford CS229 Projects
If you happen to have any idea you would like to work on, we are open to any kind of project proposal as long as it stays within the scope of the course. Examples of such ideas could revolve around more theoretical projects such as optimization, interpretability, etc.
- Project proposal, due by March 6, 2020 at 23:59.
- Project code and report, due by May 22, 2020 at 23:59.
- Project presentations (TBD).
Projects should be submitted through the Montefiore submission platform.
- Students can work in groups of maximum 3 students.
- Each group must write a short (1-2 pages) research project proposal. It should include a description of a minimum viable project, the data you will use or collect, the computing resources you think you will need, some nice-to-haves if time allows, and a short review of related work.
- Towards the end of the class, you will submit a project report (around 8 pages), in the format of a machine learning conference paper which has to include the following sections:
- Introduction: which states the problem which has been tackled
- Related Work: which covers research that is related to the considered problem
- Methods: a clear and detailed description of the neural networks (architecture, training-parameters, loss function, data)
- Results:
- qualitative analysis: could include examples of generated images, correct vs wrong predictions, ...
- quantitative analysis: general overview of final performance, loss curves, comparison table with error-bars, ...
- Discussion: a critical discussion of the performance of the neural network, analysis of the potential limitations, tips for future work
- At the end of the course everyone will present their project to the class.
- The grade will depend on two main components:
- quality and originality of the project (are the contributions of the group to the development of the project well defined? what has been implemented with respect to the original research questions, what has been re-used from existing coding directories?)
- presentation of the project (structure of the report, clarity of figures/tables, correctness of the English language and the oral final presentation)
- Both the project proposal and the project report should follow the LaTex template
template-report.tex. Feel free to change the structure of the latex template if needed.
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.
If you are combining your course project with the project from another class, you must receive permission from the instructors, and clearly explain in the proposal, final report the exact portion of the project that is being counted for INFO8010. In this case you must prepare separate reports for each course, and submit your final report for the other course as well.
Credits: Project instructions partly adapted from Stanford CS231n.