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added the generic folders for code that is framework independent (e.g…
…., uses only the numpy library) or can support all major deep leanring libraries
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# Generic Code (framework independent) | ||
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This folder contains code that is framework independent (for example, it uses | ||
the numpy library only or can handle support for all major deep learning | ||
libraries). |
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# Defenses | ||
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This folder contains implementations of defenses (as standalone scripts) in one | ||
of the three frameworks (JAX, PyTorch, TF2). Each defense should be implemented | ||
as a script that helps reproduce results reported in the paper introducing the | ||
defense. The goal is for these scripts to be authoritative ways to reproduce | ||
the defense. | ||
of the three frameworks (JAX, PyTorch, TF2) or if it is framework independent | ||
then this code should go to the generic folder. Each defense should be | ||
implemented as a script that helps reproduce results reported in the paper | ||
introducing the defense. The goal is for these scripts to be authoritative ways | ||
to reproduce the defense. |
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# Generic Defenses (framework independent) | ||
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This folder contains implementations of defenses (as standalone scripts) that | ||
are implemented in a framework independent way (for example, it uses the numpy | ||
library only or can handle support for all major deep learning libraries). |
Empty file.
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# Tutorials for framework independent features | ||
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This folder contains implementations of features that are implemented in a | ||
framework independent way (for example, it uses the numpy library only or can | ||
handle support for all major deep learning libraries). |
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