Huh? It appears to be written in PyTorch according to the article?
The training data could also be considered source.
And I agree that this is of limited use if I have to access it by uploading and downloading everything from Facebook servers. Not only do I have privacy implications, but there's the need for a solid fast low latency internet connection that I can't guarantee.
> The training data could also be considered source.
That makes me wonder about something: would it be less computationally intensive to prove that a trained net is the result of some training data than the training procedure? A lot less? Can it be proven that there is no backdoor (supplemental training data altering the results)?
I suspect you could prove rather cheaply that the trained net weight indeed correspond to a local minima for the training data. However, there is no telling that this is the best minima that could be achieved, nor that the provided training dataset is enough to obrain that result.
Depending on the architecture, though, it's possible to export the trained model into a stand-alone file that can be imported by somebody else's program, de-coupling the network's training data from model it produces.
This is done pretty frequently in areas like computer vision and speech recognition, with the pre-trained weights for YOLO and Mozilla Deepspeech[0] being available for download. I'm not sure if the word "open-source" totally applies here, since as you pointed out, apart from downloading the dataset source might be tought, but OP's question might be answered by having the resulting models made publicly available with the source code of the networks they used to train and deploy it?
