 Dear Fellow Scholars, this is Two Minute Papers with Kato Jolene Fahir. Today we are going to talk about a paper that builds on a previous work by the name Deep Image Priors, Deep Inshort. This work was capable of performing JPEG compression artifact removal, image impainting, or in other words, filling in parts of the image with data that makes sense, super resolution, and image denoising. It was quite the package. This new method is able to subdivide an image into a collection of layers, which makes it capable of doing many seemingly unrelated tasks. For instance, one, it can do image segmentation, which typically means producing a mask that shows us the boundaries between the foreground and the background. As an additional advantage, it can also do this for videos as well. Two, it can perform the hazing, which can also be thought of as a decomposition task where the input is one image, and the output is an image with haze, and one with the objects hiding behind the haze. If you spend a tiny bit of time looking at the window on a hazey day, you will immediately see that this is immensely difficult, mostly because of the fact that the amount of haze that we see is non-uniform along the landscape. The AI has to detect and remove just the right amount of this haze and recover the original colors of the image. And three, it can also subdivide these crazy examples where two images are blended together. In a moment, I'll show you a better example with a complex texture where it is easier to see the utility of such a technique. And four, of course, it can also perform image-in-painting, which, for instance, can help us remove watermarks or other unwanted artifacts from our photos. This case can also be thought of as an image layer, plus a watermark layer, and the algorithm is able to recover both of them. As you see here on the right, a tiny part of the content seems to bleed into the watermark layer, but the results are still amazing. It does this by using multiple of these dips, deep image prior networks, and goes by the name DoubleDip. That one got me good when I first seen it. You see here, how it tries to reproduce this complex textured pattern as a sum of these two much simpler individual components. The supplementary materials are available right in your browser and show you a ton of comparisons against other previous works. Here you see the results of these earlier works on image-dhasing and see that indeed the new results are second to none. And all this progress within only two years. What a time to be alive. If like me, you'll have information theory. Woohoo! Make sure to have a look at the paper and you'll be a happy person. This episode has been supported by weights and biases. Weight and biases provides tools to track your experiments in your deep learning projects. It is like a shared logbook for your team, and with this, you can compare your own experiment results, put them next to what your colleagues did, and you can discuss your successes and failures much easier. It takes less than five minutes to set up and is being used by OpenAI, Toyota Research, Stanford, and Berkeley. It was also used in this OpenAI project that you see here, which we covered earlier in the series. They reported that experiment tracking was crucial in this project, and that this tool saved them quite a bit of time and money. If only I had access to such a tool during our last research project where I had to compare the performance of neural networks for months and months. Well, it turns out I will be able to get access to these tools because get this, it's free, and will always be free for academics and open source projects. Make sure to visit them through whendb.com slash papers, wamdb.com slash papers, or just click the link in the video description and sign up for a free demo today. Our 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.