Comment by is74

Many comments expressed concern about the alleged inappropriateness of the title. Even the no-free lunch theorem has been invoked, and words like SVM mentioned.

However: The original title, "Neural Networks officially best at object recognition", is much more appropriate than the current title, because it is by far the hardest vision contest. It is nearly two orders of magnituder larger and harder than other contests, which is why the winner of this contest is best at object recognition. The original title is much more accurate and should be restored.

Second, the gap between the first and the second entry is so obviously huge (25% error vs 15% error), that it cannot be bridged with simple "feature engineering". Neural networks win precisely because they look at the data, and choose the best possible features. The best human feature engineers could not come close to a relentless data-hungry algorithm.

Third, there was mention of the no-free lunch theorem and of how one cannot tell which methods are better. That theorem says that learning is impossible on data that has no structure, which is true but irrelevant. What's relevant that on the "specific" problem of object recognition as represented by this 1-million large dataset, neural networks are the best method.

Finally, if somebody makes SVMs deep, they will become more like neural networks and do better. Which is the point.

This is the beginning of the neural networks revolution in computer vision.