Does Deep Learning Have Deep Flaws?
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Does Deep Learning Have Deep Flaws?
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Re: Does Deep Learning Have Deep Flaws?
#2But then we wouldn't see faces in clouds...
Neutral networks are not perfect solutions, they are solutions that get an organism to reproduce successfully.
Read any book on color vision humans have similar problems, yet for the most part we see things, and realize that clouds are just clouds and not faces, except for the religious, they lose their shit when faces appear in clouds.
Re: Does Deep Learning Have Deep Flaws?
#3First thought:
Can I turn all digital pictures of me into 'adversarial examples', so the eye of sauron can't identify me from pictures?
I'm sure it's not as simple as that, presumably any algorithmic modification to an 'adversarial' nature can be countered by other algorithms.
But I predict a new realm of 'arms race' here in the future.
Re: Does Deep Learning Have Deep Flaws?
#4I wonder if subtle make up could be used to make a real life face into an adversarial example.
Re: Does Deep Learning Have Deep Flaws?
#5I 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 model, possibly by using another model.
Re: Does Deep Learning Have Deep Flaws?
#6First thought: Can I turn all digital pictures of me into 'adversarial examples', so the eye of sauron can't identify me from pictures? I'm sure it's not as simple as that, presumably any algorithmic modification to an 'adversarial' nature can be countered by other algorithms. But I predict a new realm of 'arms race' here in the future.
Adversarial examples are tied to a specific algorithm, they didn't produce any universal adversarial examples from what I understand.
Re: Does Deep Learning Have Deep Flaws?
#7IIRC even the human brain has the 'adversarial' image flaw (these images will be unique to each person), but one simple workaround is to alter the input image via eye movement (which happens unconsciously).
Re: Does Deep Learning Have Deep Flaws?
#8The key claim, from the original paper:
> Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous [...] Specifically, we find that we can cause the network to misclassify an image by applying a certain imperceptible perturbation [...] the same perturbation can cause a different network that was trained on a different subset of the dataset, to misclassify the same input.
It's an interesting outcome -- but there are many deep-learning approaches and many different benchmarks, so it will be important to see if this is a misleading anecdote or indicative of a systematic problem.
Re: Does Deep Learning Have Deep Flaws?
#9I wonder if subtle make up could be used to make a real life face into an adversarial example.
You can apply facial makeup that makes your face look completely different to a human observer. Trying to understand how a person with full-coverage facial makeup really looks like is a very frustrating experience. You're trying to see the contours of the face, but your brain keeps focusing on the applied colors. The same makeup would probably work well on a computer as well.
Re: Does Deep Learning Have Deep Flaws?
#10I 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 value proposition of deep learning is to eliminate these kind of hand-coded features and to discover the features automagically.
However, maybe there's a middle ground. I.e. maybe we don't need the more esoteric features that SIFT uses, but it just makes sense to do edge detection, and say a Fourier transform for audio.