StyleGAN2: Near-Perfect Human Face Synthesis...and More

dear fellow scholars this is two minute papers with károly fajir neural network based learning algorithms are on the rise these days and even though it is common knowledge that they are capable of image classification or in other words looking at an image and saying whether it depicts a dog or a cat nowadays they can do much more in this series we covered a stunning paper that showcased the system that could not only classify an image but write the proper sentence on what is going on and could cover even highly non-trivial cases you may be surprised but this thing is not recent at all this is four year old news insanity later researchers turned this whole problem around and performed something that was previously thought to be impossible they started using these networks to generate photorealistic images from a written text description we could create new bird species by specifying that it should have orange legs and a short yellow bill later researchers at Nvidia recognized and addressed two shortcomings one was that the images were not that detailed and two even though we could input text we couldn't exert too much artistic control over the results in came style Gant to the rescue which was able to perform both of these difficult tasks really well these images were progressively grown which means that we started out with a course image and go over it over and over again adding new details this is what the results look like and we can marvel at the fact that none of these people are real however some of these images were still contaminated by unwanted artifacts furthermore there are some features that are highly localized as we exert control over these images you can see how this part of the teeth and eyes are pinned to a particular position and the algorithm just refuses to let it go sometimes to the detriment of its surroundings this new work is titled sty again - and it addresses all of these problems in one go perhaps this is the only place on the Internet where we can say that finally teeth and eyes are now allowed to float around freely and mean it with a positive sentiment here you see a few hand-picked examples from the best ones and I have to say these are i popping lee detailed incorrect looking images my goodness the mixing examples you see here are also outstanding way better than the previous version also note that as there are plenty of training images out there for many other things beyond human faces it can also generate cars churches horses and of course cats now that the original stag and one work has been out for a while we have a little more clarity and understanding as to how it does what it does and the redundant parts of the architecture have been revised and simplified this clarity comes with additional advantages beyond faster and higher quality training and image generation for instance interestingly despite the fact that the quality has improved significantly images made with a new method can be detected more easily note that the paper does much more than this so make sure to have a look in the video description in this series we always say that two more papers down the line and this technique will be leaps and bounds beyond the first iteration well here we are not two but only one more people down the line what a time to be alive the source code of this project is also available what's more it even runs in your browser this episode has been supported by weights and biases provides tools to track your experiments in your deep learning projects it can save you a ton of time and money in these projects and is being used by open a Toyota Research Stanford and Berkeley here you see a beautiful final report on one of their projects on classifying parts of street images and see how these learning algorithms evolve over time make sure to visit them through wendy b. com slash papers or just click the link in the video description and you can get a free demo today or thanks to weights and biases for helping us make better videos for you thanks for watching and for your generous support and I'll see you next time