The code and data is here:

https://github.com/KamitaniLab/DeepImageReconstruction

Not a hoax.

Your comparison to Deep Dream is quite apt, for good reason that you clearly don't realize-

Deep Dream is an attempt to "enhance patterns in images via algorithmic pareidolia"- Pareidolia being a "psychological phenomenon in which the mind responds to a stimulus". So yes, this project is affected by a real life "deep dream".

That's exactly what they say they're doing. That's not "contamination" - that's the whole point.

And that's exactly why this is garbage research. Point deep dream at an interference pattern of pure noise, after training it on a set of target images.

Don't be surprised when it "finds" the image, pulled from raw static that contained no signal.

Not convinced this is as junk as you claim it to be. From the abstract:

> While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed reconstructs or generates images from brain activity, not simply matches to exemplars.

If I am reading that correctly it is reconstructing images that the subjects had not seen before.

Not my field of expertise but on page 15 they claim " In our reconstruction analysis, we used a pre-trained DGN which was provided by Dosovitskiy & Brox" So if the deep dream generator is not trained on their actual (limited) training data but instead on a massive dataset of 'all photos that are real", I would say the study is valid.

Don't feed trolls with throwaway accounts.