4 ms·
The article references the famous face-depixellating PULSE GAN. Let's remember that PULSE also gave us Barry O'Bama. https://www.google.com/amp/s/www.theverge
by Pxtl 5y ago
The article references the famous face-depixellating PULSE GAN.
Let's remember that PULSE also gave us Barry O'Bama.
https://www.google.com/amp/s/www.theverge.com/platform/amp/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias https://www.google.com/amp/s/www.theverge.com/platform/amp/2...
- pc86 5y agoNon-AMP link: https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias https://www.theverge.com/21298762/face-depixelizer-ai-machin...
- rndgermandude 5y agoIndeed, however one has to note that this article is mainly about pixelated text, which is a bit different, especially considering that it (usually) has a known alphabet and you often even know what the individual font glyphs look like from context.
- hwers 5y agoYou definitely shouldn't be downvoted, this is a very valid point.
- Pxtl 5y agoI assume I got down-voted by people cranky about AMP links.
- rndgermandude 5y agoI basically only saw "Barry O'Bama" at first, and thought "oh good, another rightwingey troll/rant". I managed to pick up on the PULSE GAN before my mouse made it over to the downvote button, tho, so it wasn't me. I think you'd be better off just adding half a sentence of tl;dr like "... Barry O'Bama, where PULSE GAN incorrectly reconstructed a pixelated image of Barack Obama, resulting in a white guy that didn't look anything like the original" or something like that.
- deleted 5y ago[deleted]
- 0des 5y agoHow can we avoid comments like these in the future?
- godelski 5y agoI'm not sure why PULSE is being called out here. This is extremely common on any super-resolution trained on extremely biased datasets like FFHQ. In fact, this discussion is something that led to the dataset being changed. The authors actually extended their research to discuss the bias[0]. I'll also link to the Reddit discussion[1]. IMO the authors here responded to this correctly. It is important to remember that algorithms are only good on their in distribution datasets. And kudos on the authors for doing more experiments and including work on a less racially and sexually biased dataset. [0] section 6 https://arxiv.org/pdf/2003.03808.pdf https://arxiv.org/pdf/2003.03808.pdf [1] https://www.reddit.com/r/MachineLearning/comments/hk2ryn/d_how_would_you_fix_pulse/ https://www.reddit.com/r/MachineLearning/comments/hk2ryn/d_h...