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DatasetGAN: Efficient Labeled Data for AI Training
- deleted 5y ago[deleted]
- etaioinshrdlu 5y agoIt 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.
- albertTJames 5y agoThis is the best role the Shanara lead actor got after the series.
- Scene_Cast2 5y agoMy intuition is that in such a setup, the discriminator can only be as good as your generator. This work provides a nice general-purpose generator, but I'm not sure as to how far you can take this (especially since vision nets don't really operate like human vision)
- hahajk 5y agoThis looks like it advocates hooking the output of one network up to the input of another, which seems like a very roundabout way of copying the weights from one network to another.
- johndough 5y agoLooks a lot like this [1] paper from a few weeks ago (which has actual code). [1] "Repurposing GANs for One-shot Semantic Part Segmentation" https://repurposegans.github.io/ https://repurposegans.github.io/