Another way adversarial training might be useful, that’s related to (1), is that it may make interpretability easier. Given that it weeds out some non-robust features, the features that remain (and the associated feature visualizations) tend to be clearer, cf e.g. Adversarial Robustness as a Prior for Learned Representations. One example of people using this is Leveraging Sparse Linear Layers for Debuggable Deep Networks (blog post, AN summary).
The above examples are from vision networks—I’d be curious about similar phenomena when adversarially training language models.
Software: streamlit.io
Need: making small webapps to display or visualize results
Other programs I’ve tried: R shiny, ipywidgets
I find streamlit extremely simple to use, it interoperates well with other libraries (eg pandas or matplotlib), the webapps render well and are easy to share, either temporarily through ngrok, or with https://share.streamlit.io/.