

Large Scale Visual Recognition Challenge 2011 - Results - is74
http://vision.stanford.edu/imagenet/ilsvrc2012/results_prelim.html

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pmelendez
I don't think this proves a superiority of any algorithm against other. Just
that SuperVision team did a great job on task 1 and task 2. I just would add
two things: 1) There is a No Free Lunch Theorem
(<http://en.wikipedia.org/wiki/No_free_lunch_theorem>) that had been applied
to pattern recognition too and that states that there is not a significative
difference in performance between most pattern recognition algorithms.

2) There is way more chance to get an increment on performance depending of
the choose of the features being used, and that seems to be the case here.

~~~
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.

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iandanforth
Hinton's team (SuperVision) uses an interesting 'dropout' technique. He gave a
Google Tech Talk on this back in June.

[http://www.youtube.com/watch?v=DleXA5ADG78&feature=plcp](http://www.youtube.com/watch?v=DleXA5ADG78&feature=plcp)

And an older talk that covers some of what a deep convolutional net is:

<http://www.youtube.com/watch?v=VdIURAu1-aU>

~~~
modeless
Hinton is currently teaching a Coursera class on neural nets:
<https://class.coursera.org/neuralnets-2012-001/class/index>

So far I've watched the first lecture and it seems like it'll be exactly the
course I've been wanting: starting with the basics of machine learning but
quickly diving into the state of the art for neural nets.

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aroberge
Sensational title that misrepresent the results of a competition with limited
(albeit high quality) participants. There is limited information of general
value in this link.

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sumodds
Am not sure if you can apply winner takes all for such marginal difference in
error. Give a slightly different database and things go awry.

Check out : "Unbiased Look at Dataset Bias", A. Torralba, A. Efros,CVPR 2011.

~~~
jules
The difference in error between the first and the rest is ENORMOUS.

Task 1:

    
    
        1st 0.15315 (convolutional neural net)
        2nd 0.26172
        3rd 0.26979
        4th 0.27058
        5th 0.29576
        [...]
    

Differences:

    
    
        0.10857
        0.00807
        0.00079
        0.02518
    

As you can see the first is way ahead of the rest. The difference between the
1st and 2nd is ~11%, between the second and third ~1%.

Task 2:

    
    
        1st 0.335463 (convolutional neural net)
        2nd 0.500342
        3rd 0.536474
    

Idem dito.

But the most exciting thing is that the results were obtained with a
relatively general purpose learning algorithm. No extraction of SIFT features,
no "hough circle transform to find eyes and noses".

The points of the paper you cite are important concerns, but this result is
still very exciting.

~~~
modeless
_the results were obtained with a relatively general purpose learning
algorithm. No extraction of SIFT features, no "hough circle transform to find
eyes and noses"._

This deserves even more emphasis. _All_ of the other teams were writing tons
of domain specific code to implement fancy feature detectors that are the
results of years of in-depth research and the subject of many PhDs. The
machine learning only comes into play after the manually-coded feature
detectors have preprocessed the data.

Meanwhile, the SuperVision team fed raw RGB pixel data directly into their
machine learning system and got a _much_ better result.

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gobengo
I found the title of this post really ironic.

"There is now clearly an objective answer to which inductive algorithm to use"

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freyr
Neural Networks officially best at object recognition _in this particular
competition of seven teams, on two of the three tasks._

Not to take away from the accomplishment of the SuperVision team, but claim in
the title seems somewhat sensationalist. Is this competition like the world
cup of object recognition or something?

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pmelendez
Just to add sense for newcomers, the original title of the thread was "Neural
Networks officially best at object recognition" and most of the posts in here
debated that the title was not appropriate for the link.

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fchollet
Congrats to the awesome folks at ISI for scoring 1st at task 3 and 2nd at task
1! Keep rocking my world.

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xenonite
why isn't there any solution of task 3 from team SuperVision with their Neural
Nets?

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anjc
*this implementation of a neural network designed for object recognition for this particular challenge

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utopkara
So, this is what HN posts have come to? The level of tabloid science news
coverage.

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Evbn
The title has changes at least twice, confusing discussion. Can we have a
title history on HN posts? Mutable state stinks.

