Unmasking Clever Hans Predictors and Assessing What Machines Really Learn — mentioned by gwern

Thu, 24 Sep 2020 15:16:35 GMT

Yes, because your validation set performance is imperfectly correlated with performance on more samples (because it's of finite size), so it will tend to overestimate within-distribution. There's also the problem that you probably want to deploy it on data which was not exactly collected the same way. So you have issues of both internal and external validity. In practice, at least for CNN classifiers on ImageNet etc, it seems to be a pretty minor issue overall. Some links: https://arxiv.org/abs/1711.11561 https://arxiv.org/abs/1806.00451 https://arxiv.org/abs/1902.10811…

Paper and discussion history. A mention is not an endorsement.

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn · HackerPapers discussions

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn — mentioned by gwern