Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution

T. Poggio observes, analyzes and predicts the evolution of deep learning from both mathematical and biological sides(which is the focus in our article) in [1]"Deep learning: mathematics and neuroscience". He mentions that, "it is telling that several of the algorithmic tricks that were touted as breakthroughs just a couple of years ago are now regarded as unnecessary ", while " some of the other ideas " such as residual learning " are more fundamental" "and likely to be more durable, though their exact form is bound to change somewhat " . In a word, he predicts that residual learning is a more durable component within the evolution of deep learning.