We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.
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I disagree: some "collective overfitting" happens when everyone evaluates on the same dataset (and often, the same test set).
There's a neat set of papers by Recht et al. showing results are slightly overfit to the test partitions of ImageNet and CIFAR-10: rotating examples between the train and test partitions causes systems to perform up to 10-15% worse.
https://arxiv.org/abs/1806.00451
https://arxiv.org/abs/1902.10811
There's another neat bit of work involving MNIST. The original dataset (from the mid-90s) had 60,000 test examples, but the distributed versions that virtually everyone uses has only 10,000 test examples. Performance on these held-out examples is, unsurprisingly, a bit worse:
https://arxiv.org/pdf/1905.10498.pdf
Also see:
Do ImageNet Classifiers Generalize to ImageNet?
We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.
https://arxiv.org/abs/1902.10811
Not sure if I follow your JFT argument, but there's a large body of work on both (a) studying whether chasing ImageNet accuracy yields models that generalize well to out of distribution data [1, 2, 3] and (b) contextualizing progress on ImageNet (i.e., what does high accuracy on ImageNet really mean?) [4, 5, 6].
For (a), maybe surprisingly the answer is mostly yes! Better ImageNet accuracy generally corresponds to better out of distribution accuracy. For (b), it turns out that the ImageNet dataset is full of contradictions---many images have multiple ImageNet-relevant objects, and often are ambiguously or mis-labeled, etc---so it's hard to disentangle progress in…
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identifying objects vs. models overfitting to the quirks of the benchmark.
[1] ObjectNet: https://objectnet.dev / associated paper
[2] ImageNet-v2: https://arxiv.org/abs/1902.10811
[3] An Unbiased Lookat Dataset Bias: https://people.csail.mit.edu/torralba/publications/datasets_... (pre-AlexNet!)
[4] From ImageNet to Image Classification: https://arxiv.org/abs/2005.11295
[5] Are we done with ImageNet? https://arxiv.org/abs/2006.07159
[6] Evaluating Machine Accuracy on ImageNet: http://proceedings.mlr.press/v119/shankar20c.html
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 http://gradientscience.org/data_rep_bias/ http://gradientscience.org/data_rep_bias.pdf https://arxiv.org/abs/1905.10498 https://arxiv.org/abs/2002.02559 https://arxiv.org/abs/2006.07159 https://arxiv.org/abs/1805.08974 https://arxiv.org/abs/1902.10178 https://arxiv.org/abs/1912.11370#google
https://arxiv.org/abs/1902.10811 is an useful counterpoint to this article's comments on ImageNet overfitting (esp e.g. §3.3, "Few Changes in the Relative Order").
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task.
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.