jswulff

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 training images.

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, which also look very "textural", but lack these effects that are an result of imaging the 3D world. Interestingly, modelling these effects without realistic textures seems to result in worse performance - this is for example the case for images taken from CLEVR or generated from Minecraft, and both perform worse than the StyleGAN images.

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 potentially some very low-level assumptions over image statistics, such as a 1/f spectrum) and produces a synthetic image.

It is then possible to generate arbitrary amounts of these images as samples from the stochastic process - these images exhibit certain image-like structures (such as oriented edges), but are as a whole still random and extremely varied, which is good and necessary for the representation learning.

In terms of helping, though, it is important to note that we do not achieve state-of-the-art performance yet, and when looking at absolute performance for a task like image classification, using real images is still better. That being said, something that is in the paper but generally seems to get lost is that our representations work very well when analyzing data that is very different from normal images, such as medical images or satellite images.

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 intermediate representation of the image (think "cylindrical, brown, meaty thing"). Such a representation can then fairly easily be mapped to the concept "hot dog".

These representations can be learned from large image datasets alone (they do not require labels!). In our work we show that you don't even need real images, but that images that are generated from noise processes are enough to train such representations, and that these representations are surprisingly good for classification.

Hope this clarifies things a bit, and happy to answer any other questions!