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Training on adversarial examples doesn't solve the fundamental problem, it merely tries to plug the holes. But in such high dimensional spaces there are many ma
by pakl 10y ago
Training on adversarial examples doesn't solve the fundamental problem, it merely tries to plug the holes. But in such high dimensional spaces there are many many holes to be plugged. :)
Agreed the failure mode may seem esoteric, but note that OpenAI is making a big deal about them.
A non-esoteric way to demonstrate the lack of generalization is to feed a deep conv network real world images (from outside the dataset). Grab a camera and upload your own photo. Roboticists who try to use deep conv nets as real world vision systems see these failures all the time.
- TTPrograms 10y agoFYI, @OpenAI: "At OpenAI, we think adversarial examples are a good aspect of security to work on because they represent a concrete problem in AI safety that can be addressed in the short term." https://openai.com/blog/adversarial-example-research/ https://openai.com/blog/adversarial-example-research/ Hardly proof that deep learning is fundamentally flawed. Regarding real world issues, these issues come up when you don't separate training and test (and real world) sets properly. My worries would be with implementation.
- pakl 10y agoI'm certainly not saying that deep learning is fundamentally flawed. It's a great method, very powerful. (Excellent algorithm.) I'm saying it's not reasonable to expect good generalization in deep convnets that learn mappings from static images to human labels. (Wrong problem.)