Can a Neural Network Read Your Mind?
or Fitting Pictures to Models
Researchers from the ATR Computational Neuroscience Labs at Kyoto and Kyoto University have recently made the news. Their paper, entitled “Deep image reconstruction from human brain activity”,(released on December 30, 2017), basically claims to have developed a machine learning model that can read your mind, with sample reconstructions shown in the picture above. To understand what they mean and whether we should all be thinking only happy thoughts now, we need to start with a brief background.
How to Read the Minds of Deep Neural Networks
A big problem with neural networks has always been their opacity: while we can see the final results, it is very hard to understand what exactly is going on inside a neural network. This is a problem for all architectures, but let us now concentrate on convolutional neural networks (CNNs) used for image processing.
Very roughly speaking, CNNs are multilayer (deep) neural networks where each layer processes the image in small windows, extracting local features. Gradually, layer by layer, local features become global, being able to draw their inputs from a larger and larger portion of the original image. Here is how it works in a very simple CNN (picture taken from this tutorial, which I recommend to read in full):
In the end, after several (sometimes several hundred) layers we get global features that “look at” the whole original image, and they combine in some ways to get us the class labels (recognize whether it is a dog, cat, or a boat). But how do we understand what these features actually do? Can we interpret them?
One idea is to simply look for the images that activate specific neurons with the hope that they will have something in common. This idea was developed, among other works, in the famous paper “Visualizing and Understanding Convolutional Networks” by Zeiler and Fergus (2013). The following picture shows windows from actual images (on the right) that provide the largest possible activations for four different high-level neurons together with their pixels that contribute to these activations; you can see that the procedure of fitting images to features does produce readily interpretable pictures:
But then researchers developed another simple but very interesting idea that works well to understand the features. The whole training process in machine learning is designed to fit the features of a network into a training dataset of images. The images are fixed, and the network weights (parameters of these convolutional layers) are changing. But we can also do it the other way around: fix the network and change the image to fit what we need!
For interpretation, this idea was developed, e.g., in the work “Understanding Neural Networks Through Deep Visualization” (2015) by Jason Yosinski et al. The results look a lot like the famous “deep dreams”, and this is no coincidence; for example, here are the images designed to activate certain classes the most:
Somewhat recognizable but pretty strange, right? We will see similar effects in the “mind-reading” pictures below.
The same idea also leads to the field of adversarial examples for CNNs: now that we have learned to fit images to networks rather than the other way around, what if we fit the images to fool the network? This is how you get examples like this:
On the left, this picture shows a “benign” image, labeled and recognized as bottlecap. On the right, an adversarial image: you can’t see any noticeable difference but the same network that achieved great results in general and correctly recognized the original as a bottlecap now confidently says that it is a… toy poodle. The difference is that on the right, an adversary has added small carefully crafted perturbations that are small and look just like white noise but that are all designed to push the network in the direction of the toy poodle class.
These examples, by the way, show that modern convolutional neural networks still have a long way to go before solving computer vision once and for all: although we all know some optical illusions that work for humans, there are no adversarial examples that would make us recognize a two-dimensional picture of a bottlecap as a toy poodle. But the power to fit images to features in the network can also be used for good. Let us see how.
And Now to Mind Reading for Humans
So what did the Japanese researchers (they are the mind readers we began with) do, exactly? First, they took the fMRI of the brain of a human looking at something and recorded the features. Functional magnetic resonance imaging (fMRI) is a medical imaging technique that is taking snapshots of the brain activity based on the blood flow: when neurons in an area of the brain are active, more blood flows there, and we can measure it; fMRI is called functional because it can capture changes in blood flow in time, resulting in videos of brain activity. To get more information you can watch this explanatory video or see a sample dataset for yourself:
Since we are measuring blood vessels and not neurons, the spatial resolution of fMRI is not perfect: we can’t go down to the level of individual neurons but we can distinguish rather small brain areas, with voxel size about 1mm in each direction. It has been known for a long time that the fMRI picture contains reliable general information regarding what the person is thinking about in the scanner: emotions, basic drives, processing different inputs such as speech, music, or video etc… but the work of Shen et al. takes this to a whole new level.
Shen et al.(2017) tried to reconstruct the exact images that people in fMRI scanners were looking at. To do that, they trained a deep neural network on fMRI activity results and then tried to match the features of a new image with the features of fMRI activations. That is, they are basically doing the same thing that we discussed above: finding an input image that matches given features as well as possible. The only difference is that the features now come not from a real image processed by the CNN but from an fMRI processed by a different network (also convolutional, of course). You can see how the network gradually fits the image to a given fMRI pattern:
The authors improved their reconstruction results drastically when they added another neural network, the deep generator network (DGN), whose work is to ensure that the image looks “natural” (in technical terms, introducing a prior on the images that favors “natural” ones). This is also an important idea in machine learning: we often can make sense of something only because we know what to expect, and artificial models are no different: they need some prior knowledge, “intuition” about what they can and cannot get, to improve their outputs.
In total, here is what the architecture looks like. The optimization problem is to find an image which best fits both the deep generator network that makes sure it is “natural” and the deep neural network that makes sure it matches fMRI features:
If these results can be replicated and further improved, this is definitely a breakthrough in neuroscience. One can even dream about paralyzed people communicating through fMRIs by concentrating on what they want to say; although in (Shen et al., 2017) reconstruction results are much worse when people are imagining a simple shape rather than directly looking at it, sometimes even with imagined shapes it does seem that there is something there:
So can a neural network read your mind now? Not really. You have to lie down in a big scary fMRI scanner and concentrate hard on a single still image. And even in the best cases, it still often comes out like this, kind of similar but not really recognizable:
Still, this is a big step forward. Maybe one day.
