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It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs th
by hyperbovine 12y ago
It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs that these models have to offer (while possibly inventing a few new ones) until its performance meets or beats the current state of the art on MNIST, CIFAR, and related benchmarks. Which is to say, these models heavily overfit to a few datasets; it should not come as a big surprise when they generalize poorly. What happens now will be that this perturbed dataset will be added to the standard training corpus and the DLNs will become robust to this effect. Then someone will figure out new way to mess them up, and the process will repeat.
- anonymousDan 12y agoWould you classify this as progress?
- wantrepeneur 12y agoI don't know what papers you are reading, but you seem to have a very distorted view of the literature. How do results on image net, production speech recognition datasets, language modeling, and high resolution satellite images fit the pattern you allege? Once again HN comment quality is depressingly low when it comes to machine learning topics. But I guess to be fair the description in the original link is very confused as well and misunderstands the conclusions of the paper.