Stanford Fei-fei Li Ph.D. a letter to students, how to do research and write a good paper _email

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Stanford University Professor Li Feifei wrote a letter to her students on how to do research and write good paper.

"You are need to feel proud of your paper does just for the" the "or" week it are finished, but many for many. "-Sim Ilar words were told by many others into different disciplines, but mentioning this point more often would never make it over emphasized.
"You need to be proud of your paper, not just the day or week it was done, but the years ahead." "-Similar words have been mentioned in many disciplines, but no matter how often they mention this point is not considered too much emphasis."

De-mystifying Good and good papers
by Fei-fei Li, 2009.03.01

Please remember this:1000+ computer vision papers get published every Only 5-10 are worth reading and remembering!

Since Many of your are writing your papers now, I thought that I ' d share this thoughts with you. I probably have said all of these in various points during our group and individual meetings. But as I continue my AC reviews This is the papers and 200+ me and my AC reviews–between, which are partner Wing points just keep coming up. Not enough people conduct-class. And not enough people write good papers.

Every Project and Every paper should is conducted and written with one singular purpose: *to genuinely The field of computer vision*. So if you are conceptualize and carry out of your work, you are need to is constantly asking the yourself this question in the most Tical Way You could– "Would I work define or reshape xxx (problem, field, technique) in the future?" This means publishing papers are not about ' this has not been published or written before, let me do it ', nor is it about Let me find a arcane little problem can get me a easy poster ". It's about "If I do", I could offer a better solution to this important problem, "or" If I does this, I could add a genu Inely new and important piece of knowledge to the field. " You should always conduct and the goal that it could is directly used by many people (or industry). In other words, your topic should have many ' customers ', and your solution is the one would they to use.

A good project is not about the past (i.e. obtaining a higher performance than the previous). It's about the future (i.e. inspiring N future papers to follow and cite for you, N->\inf).

A CVPR ' submission with a Caltech101 performance to received 444 (3 weakly rejects) this year, and would be rejected. This is by far the highest performance I ' ve seen for Caltech101. So why are this paper rejected? Because it doesn ' t teach us anything, and no one would likely is using it for anything. It uses a known technique (at least for many people) with super already tweaked parameters for the dataset tha T is no longer a good reflection of Real-world image data. It uses a BoW representation without object level understanding. All reviewers (from very different angles) asked the same question "What does we learn from your?" And the only sensible answer I could come up and is this Caltech101 is no longer a good dataset.

The

Einstein used to say:everything should is made as simple as possible, and but not simpler. Your Method/algorithm should is the most simple, coherent and principled one you could to the this solving. Computer vision, like many, areas of engineering and science, are about problems, not equations. No one appreciates a complicated graphical model with super fancy inference techniques that essentially achieves the same Result as a simple svm–unless it offers deeper understanding of your data this no other simpler methods offer. A method in which your have to manually tune many parameters are not considered principled or coherent.

This might sound corny and but it is true. You ' re PhD students in one of the best universities in the world. This means your embody the highest level of intellectualism of humanity today. This means your are not a technician and your are not a coding monkey. When you write your paper, you communicate and. That ' s what a paper is about. This is what you should approach your writing. You are need to feel proud of your paper not just for the "day" or week it is finished, but many for many.

Set a high goal for yourself–the truth are, can achieve it as long as you put your heart in it! When you are your paper, ask yourself this question:is this going to be among the papers of 2009 that people would Remember in computer vision? If not, why? The truth is only 10+/-epsilon gets remembered every year. Most of the papers are just meaningless publication. A long string of mediocre papers on your resume can in best get for Google software engineer Job (if at all–2009.03 up Date:no, Google doesn ' t hire PhD for this anymore). A couple of seminal papers can get your a faculty job in a top university. This is the truth that most graduate students don ' t know, or don ' t have a chance to know.

Review process is highly random. But There is one golden rule that withstands the "test of" and randomness–badly written get bad papers. Period. It doesn ' t matter if the idea is good, the good of the result is, citations are good. Not on all. Writing is Critical–and this is ironic because engineers are the the worst trained writers among all disciplines in a univer Sity. You are need to discipline yourself:leave time for writing, and I am deeply about writing, and write it over and over again till It ' s as polished as you can.

Last but isn't the least, please remember this rule:important problem (inspiring idea) + solid and novel Theory + convincin G and analytical experiments + good writing = seminal and excellent paper. If any of the ingredients is weak, your paper, hence reviewer, scores would.

The above is reproduced.

Here are some related links to Professor Li Feifei:
1. Professor Li Feifei's homepage at Stanford: http://vision.stanford.edu/feifeili/
2. Li Feifei's business card at Stanford: Https://profiles.stanford.edu/fei-fei-li?tab=bio
3. Fei-fei Li's speech at TED: How do we teach computers to understand pictures?
Https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures?language=zh-cn
4. Imagenet Creator: Let the cold machine read the story behind the photo
Http://www.leiphone.com/news/201604/uD8o9lV0AhJRhcit.html
5. Li Feifei, associate professor of Chinese descent: Why does AI need to diversify?
Http://www.leiphone.com/news/201610/b5YO6ljHNQy3LAmb.html
6. Li Feifei announced that it was the first step in the industry to become a a16z professor at Silicon Valley's top investment agency.
Http://www.leiphone.com/news/201607/Dxptg5EciurCiAzu.html
7. How to view AI scholars "Exodus" from campus to industry.
Http://www.leiphone.com/news/201611/oU3kqSVCDxu8SgUB.html
8. Li Feifei Gaotu Andrej Karpathy for everyone to answer questions
Http://www.leiphone.com/news/201609/SlT4M0Wq1oBYOw3F.html
9. Stanford Intelligent Laboratory
http://vision.stanford.edu/
9. Yang Lan interviewed Professor Li Feifei:
http://weibo.com/1198920804/DF40mduCR?type=comment#_rnd1481863662338