Machine learning research as a product. How easy is it to use your work?

The best research is when you are answering questions that bother you and it happens to be in line with your company/grant proposal goals. However, the single core message depending on the way it is written and accompanied artifacts can influence different people, shared across different communities, live long or die fast.

How to be more useful for people and gain more citations?

General things

  • Is it easy to grasp the key message fast and present it on the reading group? If no, you are in trouble.
arxiv-sanity One of the main ways of discovering new ML papers now
  • Add experiments on as various datasets as possible. In the short term, you will gather citations by people who don`t care at all about your method, but who need to fill-in the benchmark table with recent/best results.

If you want to be useful for other researchers in ML/CV field

  • Publish source code on github. This will encourage to build on top of your method and/or improve its parts. Important: the code, which is hard to compile is almost useless, as no code at all. The best is to provide python pip package.
screenshot from

If you want to be useful for applied researchers in other fields

Create end-user-task-examples

Basically, this is the “product” part. The most of people including researchers are not interested in your paper or your domain. But they all have their problems and tasks.

If your method can be easily integrated with some other libraries to produce some meaningful result for end-user, be it doctor, historian or modeler — just do it.

E.g. if you are working with local features, provide a tool to compare two images and output list of correspondences + visualization, like ASIFT or MODS.

ASIFT web-demo for image matching

Provide Windows binaries/installation files

Lots of researches from non-ML field work on Windows. Give them an opportunity to try your tool!

If it is worth the game, create the video tutorial for users. COLMAP is a great example of both

COLMAP has great tutorials.

These all are quite simple and obvious things, but you will be surprised how many researchers neglect them. Good luck!

Computer Vision researcher and consultant. Co-founder of Ukrainian Research group “Szkocka”.

Computer Vision researcher and consultant. Co-founder of Ukrainian Research group “Szkocka”.