No, but independent third party verification is a cornerstone of science.

I read many many deep learning papers and very often there are cherry picked results OR poor validation methods. For example, it's common for naive/inexperienced people to predict on the same data that the network is trained on : because it gives much better results due to overfitting.

I'm not saying that's what this paper has done. But if you are indeed going to publish, then your experiment needs to be reproducible, this means including the source code and the data.

The snark is unnecessary. Many papers in deep learning display heavily cherry-picked results. Including a source code implementation is one way to ensure that the real world results match the cherry picked results

No, that's unlikely. But it does mean that independent verification is difficult, if not impossible, given that we don't have their implementation or working data set.

Right, nobody would ever lie or stretch the truth right?

Hehe