

ConvNet Features off-the-shelf: an Astounding Baseline for Object Recognition - m_ke
http://arxiv.org/abs/1403.6382

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m_ke
Quick summary for people who don't follow machine learning and computer
vision:

A team from NYU recently open sourced their neural net model from a recent
object recognition competition. The team from KTH used that pre-trained net as
a feature extractor and applied it to other standard datasets by learning an
SVM on the extracted features. This "simple" approach did as well if not
better than most state of the art methods, which were hand engineered for the
specific tasks.

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therobot24
CNNs are great, but so prone to overfitting, thankfully that last few years
have been good to the deep learning community - dropout, jitter, etc. - in
combating these problems. Hopefully in the next few years more books are
published to help dumb-down the material since reading a lot of these papers
(in terms of trying to implement/expand/critique the material - just the
history alone is interesting - hopfield nets, perceptrons, hebbian learning,
... ) is still very tough as results are difficult to reproduce

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maurits
There is a nice python wrapper, called nolearn [1] that uses a pre-trained CNN
called DeCaF to extract features prior to the final classification layer. I
found the results to be surprisingly good in a lot of situations.

[1]: [https://pythonhosted.org/nolearn/](https://pythonhosted.org/nolearn/)

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asgard1024
I have to wonder - are there some techniques to build the trained models by
crowdsourcing?

