Do Neural Networks Need To Think Like Humans?

dear fellow Scholars this is 2minute papers with car here as convolutional neural network based image classifiers are able to correctly identify objects in images and are getting more and more pervasive scientists at the University of tubingen decided to embark on a project to learn more about the inner workings of these networks their key question was whether they really work similarly to humans or not now one way of doing this is visualizing the inner workings of the neural network this is a research field on its own I try to report on it to you every now and then and we talked about some damn good papers on this with more to come a different way would be to disregard the inner workings of the neural network in other words to treat it like a black box at least temporarily but what does this mean exactly let's have a look at an example and in this example our test subject shall be none other than this cat here we have a bun of neural networks that have been trained on the classical image net data set and a set of humans this cat is successfully identified by all classical neural network architectures and most humans now onwards to a grayscale version of the same cat the neural networks are still quite confident that this is a cat Some Humans faltered but still nothing too crazy going on here now let's look at the silhouette of the cat whoo suddenly humans are doing much better at identifying the cat than neural networks this is even more so true when we are only given the edges of the image however when looking at a heavily zoomed in image of the texture of an Indian Elephant neural networks are very confident with their correct guess where Some Humans falter ha we have a lead here it may be that as opposed to humans neural networks think more in terms of textures than shapes Let's test that hypothesis step number one Indian elephant this is correctly identified now cat again correctly identified and now hold on to your papers a cat with an elephant texture and there we go is still a cat to us humans but is an elephant to convolutional neural networks after looking some more at the problem they found that the most common convolutional neural network architectures that were trained on the image net data set vastly overvalue textures over shapes that is fundamentally different to how we humans think so can we try to remedy this problem is this even a problem at all mural networks need not to think like humans but who knows it's research we might find something useful along the way so how could we create a data set that would teach a neural network a better understanding of shapes well that's a great question and one possible answer is style transfer let me explain style transfer is the process of fusing together two images where the content of one image and the style of the other is taken so now let's take the imag net data set and run style transfer on each of these images this is useful because it repaints the textures but the shapes are mostly left intact the authors call it the stylized imag net data set and have made it publicly available for everyone this new data set will no doubt coers the neural network to build a better understanding of shapes which will bring it closer to human thinking we don't know if that is a good thing yet so let's look at the results and here comes the surprise when training a neural network architecture by the name resnet 50 jointly on the regular and stylized imag net data set after a little fine-tuning they have found two remarkable things one the resulting neural network now sees more similarly to humans the old blue squares on the right mean that the old thinking is texture-based but the new neural networks denoted with the orange squares are now much closer to the shape based thinking of humans which is indicated with the red circles and now hold on to your papers because two the new neural network also outperforms the old ones in terms of accuracy dear fellow Scholars this is research at its finest the authors explored an interesting idea and look where they ended up amazing if you enjoy this episode and you feel that a bunch of these videos a month are worth $3 please consider supporting us on patreon this helps us get more independent and

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