[pricecomstock]

From what I understand, the training input images are just to establish the relationship between sparse data points and full image, regardless of subject matter. Since they were getting the black hole picture out of the trained model regardless of how it was trained, it's likely that the model was producing accurate results of what the "camera" was pointed at. If they had pointed it at an elephant, the model would have produced a picture of something elephant-like because it was somewhat accurately reconstructing a full image from sparse data points.

[dennisgorelik]

> getting the black hole picture out of the trained model regardless of how it was trained

Did they try to feed random noise into their trained image builder?

I suspect that the output of that trained image builder is always the same "black hole", even with random noise as an input.

[QuotedForTruth]

Probably not with random noise. With random noise there is literally no connection between pixels. With any actual picture there are connections. Like for instance a pixel is more likely to be the value of its neighbor or nearly so than any random value. This follows from the fact that the pictures are of actual objects with physical properties that determine the value of the pixel that maps to them. Most of the image can be characterized by continuous gradients with occasional edges.

I think if you trained with random noise you would get random noise output.