sent2null space

So the last few years has seen impressive performance in machine learning models leveraging deep model processes involving multiple layers of neural networks emerging an ability to highly characterize a target image in the "style" of a given input image to produce an output image that appears as if it were created in an artistic way by the algorithm. The apps. and filters leveraging these neural networks (convolutional being the ones most effective at this proto creative action) are quickly appearing in various apps. However, for creating art....particularly creating novel art that is not just the result of a complex mathematical process against a single source and a single target image....such approaches are an utter failure....for example, as an illustrator I can be given two or 3 input images of a given character from different perspectives and on the basis of that small set of input create a wide variety of new images ....of that same character with

The last 5 years have been a great awakening in the space of machine learning and the subset discipline that up to this point had been called AI. When Hinton et al discovered the power of GPU's toward improving the training rate of their neural network models they allowed radical improvements in experimentation using those models while also enabling them to train on vastly larger data sets then were practical (read: cost effective) in the past. The later innovation of drop out as a means of reducing over fitting in the trained results of such networks allowed older models to be significantly more flexible in avoiding over focus on features in a given data set that made the final trained network too specific to the original trained set. The extension of neural network models to incorporate GPU acceleration , drop out and multiple layers then enabled the exploration of neural network approaches of all manner of real world training scenarios that model more closely than ever