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This actually kind of freaks me out. Might it be possible that there is a way to corrupt brains?
We are well aware of various optical illusions, but are there "illusions" that be applied against other brain structures, such as memory or beliefs?
In audio, we have the Shepard tone (http://en.wikipedia.org/wiki/Shepard_tone) as an auditory illusion.
Not an example from deep learning, but [1] also demonstrates that Bayesian systems also have similar problems with sensitivity to initial conditions that are quite similar. It is also rather striking that these DLNs seem to be tricked by what we would typically think of as noise. 1. http://arxiv.org/abs/1308.6306
It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs that these models have to offer (while possibly inventing a few new ones) until its performance meets or beats the current state of the art on MNIST, CIFAR, and related benchmarks. Which is to say, these models heavily overfit to a few datasets; it should not co…
Earlier quoted context omitted.
From the article: What’s more surprising is that the same perturbation can cause a different network, which was trained on a different training dataset, to misclassify the same image. It means that adversarial examples are somewhat universal.
Indeed, but they change the dataset and keep the same algorithm. I didn't see anything about changing the algorithm in itself. That's not very surprising: if you have enough training data, any large training set will be typical because it'll be average enough. But when you choose a specific algorithm, you can exploit its weaknesses and throwing more data won't change anything. Change the algorithm will, though.
Like if you fit 5 close-to-linear 2d points with a 4th order polynomial, you'll overfit. Change the data slightly and you'll still overfit, but your fit will be very different.
This problem was observed 20+ years ago with linear models used for protein structure prediction. For any given model of what described a properly folded protein, one could locate conformations of the same protein that were rated as folded even better than the correct conformation (I called them doppelgangers, but the name "decoy" is what caught on). The statistical naivete of the field led to all sorts of inadverten…
Not an example from deep learning, but [1] also demonstrates that Bayesian systems also have similar problems with sensitivity to initial conditions that are quite similar. It is also rather striking that these DLNs seem to be tricked by what we would typically think of as noise. 1. http://arxiv.org/abs/1308.6306
I think just like any machine learning algorithm, especially with computer vision, you need to prepare things properly. More robust data and images such as moving window on image patches and image rotations, even going as far as applying filters like sobel for edge detection will make your algorithm a lot better. Any algorithms have weaknesses. It's a matter of being aware of them and compensating for them in your mo…
The problem raised in the paper is that most of the time, you can safely assume that if you have a new data point that is very close to a bunch of correctly-classified data points and far away from points of different classes, the new data point will be correctly classified. In other words, you assume the classification probability is locally smooth. The problem is that the adversarial examples they are able to come…
To accomplish that, for example, they could take perturbations of the input and then e.g. take a majority majority vote.
Earlier quoted context omitted.
I'm knowingly being a pedant and I apologize for that but they don't quite say that, rather they come awfully close to doing so. And I'm being a pedant because of all the low information sorts claiming this is a refutation of deep neural networks (it's not, well at least not yet). "The above observations suggest that adversarial examples are somewhat universal and not just the results of overfitting to a particular m…
So, the other interesting paper which I failed to cite was this one: http://www.ncbi.nlm.nih.gov/pubmed/24265211 in which we showed that rosetta needed to include bond angle terms to accurately model some proteins. That said, I'm a bit surprised you found what you did about AMBER (and other force fields), or rather, that you didn't publish. The cornell et al force field was later acknowledged to have serious problems…
Also, the really interesting thing was that adversaries generated for one network topology/data set were still adversarial even for other network topologys/data sets, which might imply that the nature of the adversaries is universal rather than highly specific to that exact network trained
Not an example from deep learning, but [1] also demonstrates that Bayesian systems also have similar problems with sensitivity to initial conditions that are quite similar. It is also rather striking that these DLNs seem to be tricked by what we would typically think of as noise. 1. http://arxiv.org/abs/1308.6306
It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs that these models have to offer (while possibly inventing a few new ones) until its performance meets or beats the current state of the art on MNIST, CIFAR, and related benchmarks. Which is to say, these models heavily overfit to a few datasets; it should not co…