
Visualizing GoogLeNet Classes - mmastrac
http://auduno.com/post/125362849838/visualizing-googlenet-classes
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amelius
Nice. But for a change, I want to see a blog that explains the training of the
network, including the fine details that are necessary to reach good results.

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nl
Where does one find the ImageNet class numbers? It doesn't appear to be
[http://image-net.org/challenges/LSVRC/2014/browse-synsets](http://image-
net.org/challenges/LSVRC/2014/browse-synsets)

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matsiyatzy
You can download the file "synset_words.txt" with class numbers in caffe via
running the script ./data/ilsvrc12/get_ilsvrc_aux.sh . But here's the file
online as well : [https://github.com/sh1r0/caffe-android-
demo/blob/master/app/...](https://github.com/sh1r0/caffe-android-
demo/blob/master/app/src/main/assets/synset_words.txt)

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nl
Is there a good explanation of inception layers in GooglLeNet?

I know they are one of the major differences between it and the Oxford models,
but I don't completely understand it.

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escherize
Isn't the fact that I can see the face of a dog (in the animated 'Shetland
sheepdog' example) a sure sign of overfitting?

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317070
No, the quality of visualization and the amount of overfitting are
uncorrelated. Both an overfitted and a perfectly generalizing model will
return excellent faces of dogs. The method is however a way to tell whether
the model is underfitted (because then you won't see dog faces).

In the field of generative models, the way to tell whether a model is
'working', is by calculating the chance that an image in the validation set
could have been generated by the model. The higher the total chance of your
validation set, the better your generative model.

These 'deepdreaming' methods (and I know, because I created one of them before
Google's code was out [1]) are nice and pretty and stuff, but it's literally
impossible to tell whether the stuff they generate is a copy from the train
set or is truly original. And as long as there is no way to evaluate them
based on a validation set, there is no basis to assume that any of them is
actually working and not copying and melding images it knows.

But then again, if they are copying and melding, but we can't tell, maybe it's
good enough to pass a Turing test? After all, aren't we humans just copying
and melding the stuff we know neither?

And, the stuff these networks create are definitely good enough to be
'interesting'.

[1] [http://317070.github.io/Dream/](http://317070.github.io/Dream/)

