[zo7]

I feel like this is related to the information bottleneck idea that's been floating around for some time [1]. I only really understand both of these at a superficial level, but from what I think I understand one thing that they observed is that there are two phases when training a deep learning model: a phase which maximizes the mutual information (?) between the input and the output, and a compression phase which compresses the learned representation. In that light this work makes sense, since artifacts are essentially noise that the network would filter out in the fitting process.

Very cool work though.

[1]: https://youtu.be/bLqJHjXihK8