[HN Gopher] DatasetGAN: Efficient Labeled Data for AI Training
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       DatasetGAN: Efficient Labeled Data for AI Training
        
       Author : alok-g
       Score  : 47 points
       Date   : 2021-05-22 17:49 UTC (5 hours ago)
        
 (HTM) web link (nv-tlabs.github.io)
 (TXT) w3m dump (nv-tlabs.github.io)
        
       | Scene_Cast2 wrote:
       | My 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 wrote:
       | This 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.
        
       | albertTJames wrote:
       | This is the best role the Shanara lead actor got after the
       | series.
        
       | johndough wrote:
       | Looks 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/
        
       | [deleted]
        
       | etaioinshrdlu wrote:
       | 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 wrote:
         | I think I'd seen StyleGAN2 using like hundreds of examples! And
         | I'm pretty sure Flickr-Faces-High-Quality only uses 70k
         | examples.
        
         | hexomancer wrote:
         | Yes, but at least it's unsupervised.
        
         | sgt101 wrote:
         | I 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 wrote:
           | Last 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?
        
         | solidasparagus wrote:
         | But that's not bad compared to the cost of pixel-wise labeling
         | to get training data for segmentation.
        
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       (page generated 2021-05-22 23:01 UTC)