81–90 of 97 posts
Question: does this setup allow to decode images from the brains of different people than the one(s) it was trained for? How well does that work compared to using the same person?
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Very interesting. For my curiosity I did some searching on VGG vs ResNet. * ResNets were better at feature extraction for image clustering [0] * One of the trade-offs of ResNets seems to be their relative complexity to VGG [1] * It surprises me that ResNets aren't significantly faster to train based on the large reduction of FLOPs (from the ResNet paper VGG-19 had 19.6 billion FLOPs vs ResNet-34 with 3.6 billion FLOP…
You might enjoy this recent article on Distill: https://distill.pub/2018/differentiable-parameterizations/#s... They discuss VGG vs non-VGG architectures in the context of style transfer in Section 2, which was interesting to me.
> One thing they noticed was that using features from a pretrained ImageNet VGG-16/19 CNN from 2014 (4 years ago), like the original Gatys paper did, worked much better than anything else; indeed, almost any set of 4-5 layers in VGG would provide great features for the style transfer optimization to target (as long as they were spread out and weren't exclusively bottom or top layers), while using more modern resnets (resnet-50) or GoogLeNet Inception v1 didn't work - it was hard to find sets of layers that would work at all and when they did, the quality of the style transfer was not as good. Interestingly, this appeared to be true of VGG CNNs trained on the MIT Places scene recognition database too, suggesting there's something architectural going on which is not database specific or peculiar to those two trained models. And their attempt at an upscaling CNN modeled on Johnson et al 2016's VGG-16 for CIFAR-100 worked well too.
[0]: https://www.reddit.com/r/MachineLearning/comments/7rrrk3/d_e...
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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.
> 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.
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Harvard updated their Buzzfeedesque post on this topic [ https://www.health.harvard.edu/mind-and-mood/12-ways-to-keep... ] which spans from crossword puzzles to avoiding alcohol. But to keep it simple: read, do puzzles, stay active, do not drink or do drugs heavily. —— Everybody already has a genetic makeup that limits their health—especially the brain—to a preset level. Some people suffer from infantile amnesia—basi…
I don't really think that you can form memories only from when you are 2 years old. I have memories of when they removed the cast from my broken arm and I was probably 1 year and some months.
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I don't really think that you can form memories only from when you are 2 years old. I have memories of when they removed the cast from my broken arm and I was probably 1 year and some months.
From what little I know of memory recall, there is a very good chance that is a false memory if it happened before people generally start being able to form memories.
Acknowledging that I know nothing about psychology or neuroscience... It's interesting that they were not able to extract good images from the test when they just asked the subjects to imagine the pictures. I speculate that is because the vast majority of people are able to recognize images of things, but not produce those images. For a simple example, see this article about people trying to draw a bicycle. Before cl…
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there is also evidence this ability to conjure imagery in our minds simply does not exist for some: https://en.wikipedia.org/wiki/Aphantasia which leads me to wonder if the capability exists on a spectrum and is stronger or weaker in some. A stronger capability might correlate to a stronger 'signal' ...?
Do people actually see images of things in their head? I can close my eyes and think of something, but I won't perceive anything more than a linear description of it. At best, I can use these visual descriptions to draw an outline in my head, but it instantly fades out if I don't continuously re-draw it. Kind of like this: https://i.imgur.com/0zuPIPV.gifv
Wow, I'm calling a hoax. They produce images of dubious value, and for whatever reason, contaminate the results by mutating them with the Deep Dream stylization. This is as bad as the dead salmon results. EDIT: You can flag all you want, but look at this playback of the reconstruction: https://video.twimg.com/tweet_video/DSrWxhSVQAE2Xix.mp4 There's nothing useful happening here.
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I think what's maybe misleading here is that while there may be an accurate representation of a bicycle somewhere in someone's brain, that does not mean they're able to consciously access it and, e.g., recreate it through a task of complex motor coordination etc. (in order to produce a drawing, I mean). However, if you are using an fMRI, as in the paper, all you need is the accurate representation somewhere (and you…
I don't think the "draw a bicycle" task involves complex motor coordination. It's drawing 2 circles and ~10 straight lines. Doesn't need to be photorealistic, just in the correct configuration. For example, I cannot visualize a 13-sided polygon accurately, but I can draw one pretty easily without having any drawing skills.
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MRI is crazy expensive, but you only really need $4M for the scanner, so doable for indie rom-come as well as blockbusters!
MRI market is very small and regulated as of today. Wider adoption of reasonably simpler devices would change everything, just recall how crazy expensive computers were in 1950s. Among useful applications I would imagine, for example, the fast image-based information retrieval - e.g. useful for recommendation systems (in other words - Netflix could invest in a 'imagine anything and we find you a movie like that').