> i wonder if the average professional coder, still has. chance to beat the big teams ? The common sentiment in r/ML is that a) big labs need some accountabili

> i wonder if the average professional coder, still has. chance to beat the big teams ? The common sentiment in r/ML is that a) big labs need some accountability because they keep getting credit for work they published but never released source code and therefore third party audit is impossible *. Furthermore, Resnet-50 is back to sota because of up-to-date training protocols, and every paper that claimed superiority was using outdated training techniques (or even none at all) for the baseline. Furthermore, there are many suspicions that the current generation of DL/ML has reached a local minimum because we have collectively pushed for models, architectures, functions, and optimizers that did well on existing hardware. For example, one hypothesis is that the Adam and AdamW optimizers are doing so well on average because we optimized model architectures for them. * Before somebody claims that this is part of the research process and we should be able to validate and implement models on our own, I would like to point out that there have been multiple instances where the math appeared sound, but the implemented models were bugged, and outperformed the existing models *because* they were bugged (NB biased) and the test sets didn't capture that. When corrected, the models didn't perform any better than existing unbiased methods.