Abstract
To achieve the recognition of multi-attribute of object, I redesign
the mnist dataset, change the color, size, location of the number.
Meanwhile, I change the label accordingly. The deep neural network I use
is the most common convolution neural network. Through test, we can
conclude that we can use one neural network to recognize multi-attribute
so long as the attribute difference of objects can be represented by
functions. The Concrete network (generation network) can generate the
output which the input rarely contained from the attributes the network
learned. Its generalization ability is good because the network is a
continuous function. Through one more test, we can conclude that one
neural network can do image recognition, speech recognition, nature
language processing and other things so long as the output node and the
input node and more parameters add into the network. The network is
universal so long as the network can process different inputs. By proof,
fully connected network can do what convolution neural network and
recurrent neural network do, so fully connected network is the universal
network. The phenomenon of synesthesia is the result of multi-input and
multi-output. Connection in mind can realize through the universal
network and sending the output into input. Connection in mind is the key
of creativity, synesthesia is the assistant.