Abstract
According to kids’ learning process, an auto-encoder which can be split
into two parts is designed. 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 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.
At last, the application of the network is discussed. The network can be
used for logic generation through deep reinforcement learning. The
network can also be used for language translation, zip and unzip,
encryption and decryption, compile and decompile, modulation and
demodulation.