Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Do CIFAR-10 Classifiers Really Work on New Photos?

A simple test shows models trained on the popular CIFAR-10 image set may not do as well on new photos.
Researchers built a fresh test set of truly unseen images to check real performance, and even when the new photos match the old ones closely many classifiers lost points.
Across different models we saw a clear accuracy drop, about 4% to 10% in many cases.
Newer, higher scoring models tend to fall less, so it seems not just cheating but a fragility to tiny changes.
The same test images were used for years to pick winners, yet small shifts in pictures makes results change, and that is worrying.
The quick take: headline numbers can be brittle.
If you see a high score ask how it does on photos it never saw before.
These findings warn that even careful systems can trip over tiny data variation, so we need fresh checks more often to know what models really learned.

Read article comprehensive review in Paperium.net:
Do CIFAR-10 Classifiers Generalize to CIFAR-10?

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