I don't see how this follows. What we're not focused on is blurred, yes, but what we're focused on is at least partially related to our ability to recognize patterns (e.g. reading from peripheral vision is very difficult).
The fact that CNNs are tricked by imperceptible perturbations while the semantic content is held constant is highly informative information, don't reject it for superficial reasons.
The standard fair of CNN+RELU+Residuals are very powerful modelling tools but that also means they're prone to model degenerate regularities if they exist. This paper shows that they do exist and that these models are picking up on them at least to some significant extent.
Object detection from peripheral vision is not difficult though. I think you're overestimating how much of our vision is actually clear.
>The fact that CNNs are tricked by imperceptible perturbations while the semantic content is held constant is highly informative information, don't reject it for superficial reasons.
Yes, but that is not this paper. These perturbations are not imperceptible, and unlike other adversarial examples the model adapted well when allowed to train on them.
Also, it looks like the model did reasonable well on the random filtered versions, only failing on the blurred versions. The random filtered images looked much more corrupted to me than the blurred ones, which is consistent with blurred images being part of the training for the human visual system, but not randomly filtered ones.
Because we understand high level semantic information, which is largely robust against blurring.
>The random filtered images looked much more corrupted to me than the blurred ones, which is consistent with blurred images being part of the training for the human visual system,
It's also consistent with the neural network training on surface level regularities, which a random filter would corrupt at a much lower rate than a blur filter would.
But only when trained on it. The models trained on only one modified domain didn't generalize well to the others. Although the model trained on all datasets together generalized quite well to the corresponding test sets, that doesn't mean that there isn't some other kind of modification that can fool it.
This is what the authors say:
Our last set of experiments involves training on the fully augmented training set, which now enjoys a variance of its Fourier image statistics. We note that this sort of data augmentation was able to close the generalization gap. However, we stress that it is doubtful that this sort of data augmentation scheme is sufficient to enable a machine learning model to truly learn the semantic concepts present in a dataset. Rather this sort of data augmentation scheme is analogous to adversarial training [40, 9]: there is a nontrivial regularization benefit, but it is not a solution to the underlying problem of not learning high level semantic concepts, nor do we aim to present it as such.
To say that we compensate for the blurring is quite different than saying we can do better (than CNNs?) with blurred images because most of the visual field is blurred. Did you mean to say that we are good at visual recognition despite most of the visual field being blurred? If so, I do not think it throws much light on what is happening in CNNs.