Your wrong about most conventional algorithms being able to get better results just by increasing image size. First, theyre isn't a dataset suitable for only s

Your wrong about most conventional algorithms being able to get better results just by increasing image size. First, theyre isn't a dataset suitable for only supervised learning (the final supervised stage they - imagenet - only has a relatively small amount of images of each category). Second, higher resolution can make things worse in conventional supervised learning, you have to get more and more images to keep up with higher resolution (extra things in the image start confusing the algorithm, and even humans - more identifiable things in the photo mean you have to think more about what is the most important thing in the image). The unsupervised algorithm gets around the both problems. There existed unsupervised/clustering algorithms before, but none that worked well until geoff hinton's breakthrough in 2005 (for which he deserves a turing award).