jswulff

4 ms·

1. 1.▲
   by jswulff 5y ago

   That is correct -- by "noise", we don't meet pixel noise, but instead a stochastic process (in fact, different processes different properties which we compare in the paper) from which we can sample large amounts of varied tra
2. 2.▲
   by jswulff 5y ago

   That would indeed be an interesting thing to try, use real data, but only in terms of textures - so effects like occlusions, perspective, etc. would not be present. I would expect it to be somewhere in the ballpark of our StyleGAN images, w
3. 3.▲
   by jswulff 5y ago

   One thing to note is that here noise != Gaussian iid noise, so these are not typical white noise images. I think we were not really clear on that part, but for us noise is basically a random process, which takes a seed as input (plus potent
4. 4.▲
   by jswulff 5y ago

   Hi, author here. To hopefully clarify, our work is in the context of representation learning, which is a bit different from a "standard" classification. For example, to classify a hotdog it might be useful to first generate an int