
Approximating CNNs with Bag-of-Local-Features Models [pdf] - kawera
https://openreview.net/pdf?id=SkfMWhAqYQ
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Veedrac
I consider the paper “ImageNet-trained CNNs are biased towards texture;
increasing shape bias improves accuracy and robustness” essentially mandatory
follow-up for anyone interested in this. It goes further with its conclusions
and introduces a potential solution. By introducing the ‘Stylized-ImageNet’
dataset, they found that they can force the network to learn shape data,
improving scores even on standard ImageNet and preventing this BagNet trick
from working.

[https://openreview.net/forum?id=Bygh9j09KX](https://openreview.net/forum?id=Bygh9j09KX)

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SubiculumCode
seems like texture training would be occur after shape training.

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MAXPOOL
Open Review
[https://openreview.net/forum?id=SkfMWhAqYQ](https://openreview.net/forum?id=SkfMWhAqYQ)

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kawera
Short video discussing this paper:
[https://www.youtube.com/watch?v=QpptSohzuDo](https://www.youtube.com/watch?v=QpptSohzuDo)

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HNLurker2
+1 for the channel. Very high signal to noise ratio channels among: Brady
channel collection (numberphile, cphile etc..)

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Natsu
For anyone else who wanted it: CNN = Convolutional neural network

I somehow mis-parsed that as the news network the first time and became very
confused for a moment.

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dgregd
It seems that texture detection is for NN the simplest and easiest way to
recognize objects. I guess early animal vision system used to work this way.
Then predators and prey developed camouflage and mimicry, to fool texture
detection NNs. So there was strong evolutionary pressure to recognize shapes,
which is much harder task, but essential for survival. Probably that's why
people see in the picture a cat (predator) and ConvNet recognizes most obvious
thing (elephant) in terms of statistics.

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p1esk
I wonder if capsules are better at recognizing shapes

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jph00
The results on this paper are 4x worse than the state of the art of a couple
of years ago. Personally I think the paper oversells their results. Creating a
much worse model by making it much simpler doesn't seem that interesting, to
me...

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albertzeyer
The point is not about creating a new good model and getting good results, but
about understanding how the CNN works. And the results of this paper yield
some very interesting findings in this respect. Maybe see this two-minutes-
paper video for a short explanation:
[https://www.youtube.com/watch?v=QpptSohzuDo](https://www.youtube.com/watch?v=QpptSohzuDo)

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olliej
I read this title as being the news network first which was much more
interesting :)

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amelius
Is this closer to how real biological brains process images?

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Veedrac
No, I imagine it's fairly simple to determine that humans use nonlocal context
in image recognition.

