[deleted]
Deep image reconstruction from human brain activity (2017)
71–80 of 97 posts
Re: Deep image reconstruction from human brain activity (2017)
#72Earlier quoted context omitted.
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' ...?
I am highly skeptical of Aphantasia, as it's impossible to evaluate what someone else believes they see. I think it's much more likely that Aphantasia is people (like me) who make sharp distinctions between what they can imagine (rotating colors on shapes etc etc) and what they see when looking at something for real - e.g - the detail of a loved one's face. I challenge anyone who believes they have a highly developed…
I have this! I found out from a random daytime TV program and did some online tests. I didn’t realise until that point that other people can make clear images of things in their head.
Weirdly I’m actually quite good at drawing something from memory like a bicycle (I love Pictionary!) but I can’t actually make a image of it in my head.
Brains are strange.
Re: Deep image reconstruction from human brain activity (2017)
#73Earlier quoted context omitted.
How does one improve brain health?
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.
Re: Deep image reconstruction from human brain activity (2017)
#74From the paper: > We used the Caffe implementation (Jia et al., 2014) of the VGG19 deep neural network (DNN) model (Simonyan & Zisserman, 2015; available from https://github.com/BVLC/caffe/wiki/Model - Zoo) . All visual images were resized to 224 × 224 pixels to compute outputs by the VGG19 model . The VGG19 model consisted of a total of sixteen convolutional layers and three fully connected layers. They used a VGG-1…
Not necessarily. While ResNets are optimized for the task of achieving the best image classification accuracy, it is no guarantee that the features extracted from it will transfer well to other tasks such as this one, especially when the task is to generate images. In fact, it has been shown that pre-trained VGG networks are a lot more useful than pre-trained ResNet for other tasks other than image classification, su…
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 FLOPs) - I think people just train deeper ResNets e.g. ResNet-50
[0]: https://medium.com/@franky07724_57962/using-keras-pre-traine...
[1]: https://www.reddit.com/r/MachineLearning/comments/6e6mlf/d_i...
Re: Deep image reconstruction from human brain activity (2017)
#75Wow, 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.
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.
Re: Deep image reconstruction from human brain activity (2017)
#76Blog post that has some info on the new GAN-based mind reading ideas from last year (including this one):
Re: Deep image reconstruction from human brain activity (2017)
#77Question: 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?
Re: Deep image reconstruction from human brain activity (2017)
#78Earlier quoted context omitted.
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' ...?
I am highly skeptical of Aphantasia, as it's impossible to evaluate what someone else believes they see. I think it's much more likely that Aphantasia is people (like me) who make sharp distinctions between what they can imagine (rotating colors on shapes etc etc) and what they see when looking at something for real - e.g - the detail of a loved one's face. I challenge anyone who believes they have a highly developed…
Agreed, from the description it sounds closer to "people who are aware of the missing parts in the imagined image".
Never heard of this "condition" before. It may apply to me. I can somewhat imagine a static thing or a face but trying to "zoom in"/"rotate" or imagining smaller details completely ruins the illusion.
Re: Deep image reconstruction from human brain activity (2017)
#79Earlier quoted context omitted.
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
As far as I know, aphantasia is very real. But you can train yourself to see images in your head.
See: image streaming by Win Wenger http://winwenger.com/imstream.htm
I'd be very curious to see if you are able to see simple things in your mind's eye after practising this for a week or two
Re: Deep image reconstruction from human brain activity (2017)
#80Earlier quoted context omitted.
Not necessarily. While ResNets are optimized for the task of achieving the best image classification accuracy, it is no guarantee that the features extracted from it will transfer well to other tasks such as this one, especially when the task is to generate images. In fact, it has been shown that pre-trained VGG networks are a lot more useful than pre-trained ResNet for other tasks other than image classification, su…
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.