Two months ago I wrote a post about some recent controversies in the industry in computer vision. In this post I turn to the world of academia/research and write about something controversial that occurred there.
But since the world of research isn’t as aggressive as that of the industry, I had to go back three years to find anything worth presenting. However, this event really is interesting, despite its age, and people in research circles talk about it to this day.
The controversy in question pertains to the ImageNet challenge and the Baidu research group. Baidu is one of the largest AI and internet companies in the world. Based in Beijing, it has the 2nd largest search engine in the world and is hence commonly referred to as China’s Google. So, when it is involved in a controversy, you know it’s no small matter!
I will divide the post into the following sections:
- ImageNet and the Deep Learning Arms Race
- What Baidu did and ImageNet’s response
- Ren Wu’s (Ex-Baidu Researcher’s) later response (here is where things get really interesting!)
Let’s get into it.
ImageNet and the Deep Learning Arms Race
(Note: I wrote about what ImageNet is in my last post, so please read that post for a more detailed explanation.)
ImageNet is the most famous image dataset by a country mile. Currently there are over 14 million images in ImageNet for nearly 22,000 synsets (WordNet has ~100,000 synsets). Over 1 million images also have hand-annotated bounding boxes around the dominant object in the image.
However, when the term “ImageNet” is used in CV literature, it usually refers to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) which is an annual competition for object detection and image classification organised by computer scientists at Stanford University, the University of North Carolina at Chapel Hill and the University of Michigan.
This competition is very famous. In fact, the deep learning revolution of the 2010s is widely attributed to have originated from this challenge after a deep convolutional neural network blitzed the competition in 2012. Since then, deep learning has revolutionised our world and the industry has been forming research groups like crazy to push the boundary of artificial intelligence. Facebook, Amazon, Google, IBM, Microsoft – all the major players in IT are now in the research game, which is phenomenal to think about for people like me who remember the days of the 2000s when research was laughed at by people in the industry.
With such large names in the deep learning world, a certain “computing arms race” has ensued. Big bucks are being pumped into these research groups to obtain (and trumpet far and wide) results better than other rivals. Who can prove to be the master of the AI world? Who is the smartest company going around? Well, competitions such as ImageNet are a perfect benchmark for questions like this, which makes the ImageNet scandal quite significant.
Baidu and ImageNet
To have your object classification algorithm scored on the ImageNet Challenge, you first get it trained on 1.5 million images from the ImageNet dataset. Then, you submit your code to the ImageNet server where this code is tested against a collection of 100,000 images that are not known to anybody. What is key, though, is that to avoid people fine-tuning the parameters in their algorithms to this specific testing set of 100,000 images, ImageNet only allows 2 evaluations/submissions on the test set per week (otherwise you could keep resubmitting until you’ve hit that “sweet spot” specific to this test set).
Before the deep learning revolution, a good ILSVRC classification error rate was 25% (that’s 1 out of 4 images being classified incorrectly). After 2014, error rates have dropped to below 5%!
In 2015, Baidu announced that with its new supercomputer called Minwa it had obtained a record low error rate of 4.58%, which was an improvement on Google’s error rate of 4.82% as well as Microsoft’s of 4.9%. Massive news in the computing arms race, even though the error rate differences appear to be minimal (and some would argue, therefore, that they’re insignificant – but that’s another story).
However, a few days after this declaration, an initial announcement was made by ImageNet:
It was recently brought to our attention that one group has circumvented our policy of allowing only 2 evaluations on the test set per week.
Three weeks later, a follow up announcement was made stating that the perpetrator of this act was Baidu. ImageNet had conducted an analysis and found that 30 accounts connected to Baidu had been used in the period of November 28th, 2014 to May 13th, 2015 to make on average four times the permitted amount of submissions.
As a result, ImageNet disqualified Baidu from that year’s competition and banned them from re-entering for a further 12 months.
Ren Wu, a distinguished AI scientist and head of the research group at the time, apologised for this mistake. A week later he was dismissed from the company. But that’s not the end of the saga.
Ren Wu’s Response
Here is where things get really interesting.
A few days after being fired from Baidu, Ren Wu sent an email to Enterprise Technology in which he denied any wrongdoing:
We didn’t break any rules, and the allegation of cheating is completely baseless
Whoa! Talk about opening a can of worms!
Ren stated that there is “no official rule specify [sic] how many times one can submit results to ImageNet servers for evaluation” and that this regulation only appears once a submission is made from one account. From this he came to understand that 2 submissions per week can be made from each account/individual rather than a whole team. Since Baidu had 5 authors working on the project, he argues that he was allowed to make 10 submission per week.
I’m not convinced though because he still used 30 accounts (purportedly to be owned by junior students assisting in the research) to make these submissions. Moreover, he still admits that on two occasions the 10 submission threshold was breached – so, he definitely did break the rules.
Things get even more interesting, however, when he states that he officially apologised just for those two occasions as requested by his management:
A mistake in our part, and it was the reason I made a public apology, requested by my management. Of course, this was my biggest mistake. And things have been gone crazy since. [emphasis mine]
Whoa! Another can of worms. He apologised as a result of a request by his management and he states that this was a mistake. It looks like he’s accusing Baidu of using him as a scapegoat in this whole affair. Two months later he confirms this to the EE Times, by stating that
I think I was set up
Well, if that isn’t big news, I don’t know what is! I personally am not convinced by Ren’s arguments. But it at least shows that the academic/research world can be exciting at times, too 🙂
To be informed when new content like this is posted, subscribe to the mailing list: