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It seems like the major cost here is that you need to train a high quality StyleGAN for your dataset -- which is very data hungry - at least thousands of images
by etaioinshrdlu 5y ago
It seems like the major cost here is that you need to train a high quality StyleGAN for your dataset -- which is very data hungry - at least thousands of images for high quality results. (The famous face generator used millions of faces)
- lsb 5y agoI think I'd seen StyleGAN2 using like hundreds of examples! And I'm pretty sure Flickr-Faces-High-Quality only uses 70k examples.
- hexomancer 5y agoYes, but at least it's unsupervised.
- sgt101 5y agoI think that the idea is that you use transfer learning to bootstrap from a general gan to a gan for your domain - and then use that to generate odd data.
- johndough 5y agoLast I checked, transfer learning for StyleGAN2 still produced somewhat wonky results if the datasets were not extremely close. Have there been improvements in this field?
- sgt101 5y agoI don't know - but I guess that must be in the paper. I had intended to read this properly after I looked at it when the story came out, but I haven't had time, sorry.
- solidasparagus 5y agoBut that’s not bad compared to the cost of pixel-wise labeling to get training data for segmentation.