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An example they give is that it doesn't apply shear transforms to CIFAR-10, because there aren't sheared examples in the (test) data. That makes it less indicat
by tlb 8y ago
An example they give is that it doesn't apply shear transforms to CIFAR-10, because there aren't sheared examples in the (test) data. That makes it less indicative of real-world vision performance, given that human vision is fairly robust to shear.
An opposite sort of strategy is to apply every augmentation that doesn't increase human error rate on a test sample.
- mlthoughts2018 8y agoSure, I think the approach could work successfully in some cases, but I think specifically allowing it to learn transformations of the input color space would be quite problematic — you are essentially giving it more degrees of surface statistics freedom with which to data mine a better looking overfitting metric during validation steps.