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That's not even an argument. Here is one against the claims. Modification: There have been repeatedly successful case of transfer learning where you take a pre
by caenorst 8y ago
That's not even an argument. Here is one against the claims.
Modification: There have been repeatedly successful case of transfer learning where you take a pretrained deep neural network and train on a new dataset (even very small).
Vulnerability: you dont need the training dataset to test vulnerabilities, you just either very specific input (that you probably gonna have to create yourself because they are so specific that they are not even in the dataset) or using one input that you corrupt for attacking. And as far as I know it doesn't sound harder than making test for regular algorithms.
- jononor 8y agoThe space of possible inputs to a machine learning model is essentially infinite. This makes it a completely different ballpark than unit testing code, where a small set of tests can cover good amount of interesting cases, and where various coverage metrics can reasonably be used to estimate completeness. It is also hard to generate tests for ML models due to lack of good oracles (detectors of wrong results). Defense against adversarial inputs is still considered an open research problem, I believe.