(L-DNN), were inspired by brain neurophysiology. These deep learning algorithms separate feature training and rule training and are able to add new rule information on the fly.
While they still learn features slowly using a large and balanced data set, L-DDNs don't learn rules at this stage. And they don't need images of all known valve defects—the dataset can be relatively generic as long as the objects possess similar features (such as curves, edges, surface properties). With L-DNNs, this part of model creation can be done once, and without the help of the manufacturers.
What our hypothetical valve manufacturer needs to know is this: After the first step of feature learning is completed, they need only provide a small set of images of good valves for the system to learn a set of rules that define a good valve. There's no need to provide any images of defective valves. L-DNNs will learn on a single presentation of a small dataset using only “good" data (in other words, data about good ventilator valves), and then advise the user when an atypical product is encountered. This method is akin to the process humans use to spot differences in objects they encounter every day—an effortless task for us, but a very hard one for deep learning models until L-DNN systems came along.