Translating this to: If it was trained on a uniform corpus of pictures sampled across the space of "likely things people take pictures of" and then used on pho

Translating this to: If it was trained on a uniform corpus of pictures sampled across the space of "likely things people take pictures of" and then used on photos from that same distribution, what would you get? The answer is: The same kind of things, but with a more balanced distribution across puppies and "other things." Possibly the easiest way to think about how a deepdreamed image might look is to look at the microstructure of an image in one isolated patch, and then look at what that patch seems most like. Is a close-up, 50% crop of a button on a shirt like... a button? an eye? a donut? And then magnify the high-level concept evoked by that microstructure. The key in getting the psychedelia of the deepdreamed images is to go up the DNN to the right level. If you go all the way to the top of an Inception-style classifier, you're left with a single (or small set of) outputs: "surfer." "dog" "cat." Letting the network propagate back down based upon that gets you just an output of what the network's canonical input is for that -- see, for example, what Deepvis outputs: http://yosinski.com/deepvis http://yosinski.com/deepvis If you stop at the first level, you basically get a lot of fancy edge detectors and sharp/unsharp masks. (The first layer is a lot of convolutions, so think about the effects you get by applying a convolutional effect in Photoshop.) Can look nice, but not exciting. See the examples in the Wikipedia article on kernels: https://en.wikipedia.org/wiki/Kernel_(image_processing) https://en.wikipedia.org/wiki/Kernel_(image_processing) But if you stop in the middle, you get things that are mid-way between "pure concept" and "concrete feature", and that's where the cool happens. It's still localized within the image, but it's able to propagate up to entirely different types of images within that local region. So eyes in hands that are still kinda shaped like hands. DD trained on a uniform set of images would have much more diversity in what it "imagines" in images. Christmas trees in forests; UFOs in pancakes; and lots and lots of human faces showing up everywhere (because real photos are heavy in human faces). Faces in faces with eyes in more faces in more eyes in more faces. ahh!