Abstract
According to kids’ learning process, an auto-encoder is
designed which can be split into two parts. The two parts can
work well separately.The top half is an abstract network which is
trained by supervised learning and can be used to classify and regress.
The bottom half is a concrete network which is accomplished by inverse
function and trained by self-supervised learning. It can generate the
input of abstract network from concept or label. The network can achieve
its intended functionality through testing by mnist dataset and
convolution neural network. Round function is added
between the abstract network and concrete network in order to
get the the representative generation of class. The generation
ability can be increased by adding jump connection and
negative feedback. At last, the characteristics of the
network is discussed. The input can
be changed to any form by encoder and then
change it back by decoder through inverse function. The concrete network
can be seen as the memory stored by the parameters.
Lethe is that when new knowledge input, the
training process makes the parameters change.
