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…
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…
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…
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…
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…
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…
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…
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…