[tripzilch]

> The Google post cites several research papers that seem to provide more than enough information to replicate these results or get similar ones, which is good

I agree that it is good, but even though the scientific theories and algorithms seem to be "open", having access to both the computing power and data-sets of Google, is not.

So one could replicate these experiments, but not quite on the scale that Google does. I'm not at all sure if it's practically possible for a single (really clever) person with a high-end CPU/GPU machine (and possibly some $$$ for Cloud Computing instances), to replicate something similar to the results in this blogpost.

The recognition nets used in the blogpost seem to be trained on a tremendously high number of training examples, to give the ability to "hallucinate" (or classify) such a great variety of animal species, for instance.

[nl]

I'm not at all sure if it's practically possible for a single (really clever) person with a high-end CPU/GPU machine (and possibly some $$$ for Cloud Computing instances), to replicate something similar to the results in this blogpost.

It's very possible.

GoogLeNet[1] is an example in Caffe: BVLC GoogLeNet in models/bvlc_googlenet: GoogLeNet trained on ILSVRC 2012, almost exactly as described in Going Deeper with Convolutions by Szegedy et al. in ILSVRC 2014. (Trained by Sergio Guadarrama @sguada)

[1] http://caffe.berkeleyvision.org/model_zoo.html