Multi-attribute Recognition,the key to Universal Neural Network
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.