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Comment by jasonjayr | Hacker News Reader

This is impressive; but isn't this also susceptible to the type of bug that Xerox copiers got hit with many years ago?

https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...

Small text is indeed one of the fail cases, and more research needs to be done here (see also my other comment [1]). We mention this issue in the supplementary materials [2], and you can check out an example here at [3]

[1] https://news.ycombinator.com/item?id=23654161

[2] https://storage.googleapis.com/hific/data/appendixb/appendix...

[3] https://storage.googleapis.com/hific/userstudy/visualize.htm...

This might be a good opportunity to lead to research on a "good faith watermark" for GAN-compressed images that may include hallucinated details.

One of the things we discussed to address this is to have the ability to: a) turn off detail hallucination completely given the same bitstream; and b) store the median/maximum absolute error across the image

(b) should allow the user to determine whether the image is suitable for their use-case.

Is it possible to put the decoder into a feedback loop and search among multiple possible encodings that minimize the residual errors? Similar to trellis optimization in video codecs. http://akuvian.org/src/x264/trellis.txt

It would be possible - but by minimizing residual errors you end up in a similar regime as when minimizing MSE again, likely making reconstructions blurry!

You could use an OCR encoder and cat that to the feature vector. Great work by the way!

Agreed! Alternatively, some semantic segmentation network could be used and a masked MSE loss. In this paper, we focussed on showing the crazy potential of GANs for compression - let's see what future work brings.

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