think you can do better than that weight and biases has everything you need for your code to be reproducible without you even trying for your well-being do like me and give weights and biases a try for free with the first link below to remove an object from an image the machine needs to understand what should appear behind the subject and to do this will require having a three-dimensional understanding of the world as humans do but it doesn't have that it just has access to a few pixels in an image which is why it's so complicated whereas it looks quite simple for us that can simply imagine the depth and guess that there should be the rest of the wall here the window and etc we basically need to teach the machine how the world typically looks like so we will do that using a lot of examples of real world images so that it can have an idea of what our world looks like in the two-dimensional picture world which is not a perfect approach but does the job then another problem comes with the computational cost of using real-world images with way too many pixels to fix that most current approaches work with low quality images so a downsized version of the image that is manageable for our computers and upscale the inpainted part at the end to replace it in the original image making the final results look worse than it could be or at least they won't look great enough to be shared on instagram and have all the likes you deserve you can't really feel it high quality images directly as it will take way too much time to process and train or can you well these are the main problems the researchers attacked in this paper and here's how roman suvarov ital from samsung research introduced a new network called llama that is quite particular as you can see in image and painting you will typically send the initial image as well as what you'd like to remove from it this is called a mask and will cover the image as you can see here and the network won't have access to this information anymore as it needs to fill in the pixels then it has to understand the image and try to fill in the same pixels it thinks should fit best so in this case they start like any other network and downscale the image but don't worry their technique will allow them to keep the same quality as a high resolution image this is because here in the processing of the image they use something a bit different than usual typically we can see different networks here in the middle mostly convolutional neural networks such networks are often used on images due to how convolutions work which i explained in other videos like the one appearing on the top right of your screen if you are interested in how it works in short the network will work in two steps first it will compress the image and try to only save relevant information the network will end up conserving mostly the general information about the image like its color overall style or general object appearing but not precise details then it will try to reconstruct the image using the same principles but backward we use some tricks like skip connections that will save information from the first few layers of the network and pass it along the second step so that it can orient it towards the right objects in short it easily knows that there's a tower with a blue sky and trees called global information but it needs the skip connections to know that it's the eiffel tower in the middle of the screen that there are clouds here and there the trees have these colors etc all the fine grained details which we call local information following a long training with many examples
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