 Dear Fellow Scholars, this is two-minute papers with Carlos Zsolnai-Fahir. Assessing how similar two images are has been a long-standing problem in computer graphics. For instance, if we write a new light simulation program, we have to compare our results against the output of other algorithms and a noise-free reference image. However, this often means that we have many noisy images, but the structure of the noise is different. This leads to endless arguments on which algorithm is favorable to the others, since who really gets to decide what kind of noise is favorable and what is not. These are important and long-standing questions that we need to find answers to. In another application, we took a photorealistic material model and wanted to visualize other materials that look similar to it. However, in order to do this, we need to explain to the computer what it means that two images are similar. This is what we call a similarity metric. Have a look at this reference image and these two variants of it. Which one is more similar to it, the blurred or the worked version? Well, according to most humans, warping is considered a less intrusive operation. However, some of the most ubiquitous similarity metrics, like computing a simple per pixel difference, thinks otherwise. Not good. What about this comparison? Which image is closer to the reference? The noisy or the blurry one? Most humans say that the noisy image is more similar, perhaps because with enough patience, one could remove all the noise pixel by pixel and get back the reference image, but in the blurry image, lots of features are permanently lost. Again, the classical error metrics think otherwise. Not good. And now comes the twist. If we build a database for many of these human decisions, feed it into a deep neural network, we'll find that this network will be able to learn and predict how humans see differences in images. This is exactly what we are looking for. You can see the agreement between this new similarity metric and these example differences. However, this shows the agreement on only three images. That could easily happen by chance. So this chart shows how different techniques correlate with how humans see differences in images. The higher the number, the higher the chance that it thinks similarly to humans. The ones labeled with LPIPS denote the new proposed technique used on several different classical neural network architectures. This is really great news for all kinds of research works that include working with images. I can't wait to start experimenting with it. The paper also contains a more elaborate discussion on failure cases as well, so make sure to have a look. Also, if you would like to help us do more to spread the word about these incredible works and pick up cool perks, please consider supporting us on Patreon. Each dollar you contribute is worth more than a thousand views, which is a ton of help for the channel. We also accept crypto currencies such as Bitcoin, Ethereum and Litecoin. Details are available in the video description. Thanks for watching and for your generous support and I'll see you next time.