AdamW and Super-convergence is now the fastest way to train neural nets
This reads a bit too much like a plug for fastai’s framework, which, frankly, is not very good and training performance results are not much of a reason to care about it (for instance, adding AdamW to Keras is basically trivial).<p>In most use cases, the difference between these optimizer choices, and how they interact with initialization schemes and metaparameters, is extremely overkill. If you’re training a pretty standard variant of e.g. ResNet50, for some fairly common classification or localization task, splitting hairs between these things just doesn’t matter.<p>I actually really think these optimizer hacks are not a productive line of research for deep learning. Too many projects try to do just the slightest amount of incremental tweaks to an optimization scheme to eke out something they can call “state of the art” and milk it for conference presentations and the release of some sexy new deep learning package. But these changes are often entirely immaterial except for the very largest network training schemes, which are usually already doing their own highly customized and distributed optimization schemes anyway.<p>Especially when it’s tied to making a plug for a framework, like this is for fastai, it’s just too focused on hype and creates this peacock feather effect where everyone had to spread themselves so thin just to keep up with all this middling, incremental tweaks, that it actually very likely inhibits research that might actually produce <i>difference of kind</i> results.