DNNs with Randomly Assigned Parameters Could Classify Object Images

It was reported in some existing works that even if a random set of parameters are assigned to a given deep neural network,this DNN would still exhibit a classification ability to some extent.Could DNNs with randomly assigned parameters indeed classify object images?AlexNet is used as the model network to investigate this problem from three aspects.Firstly,a random set of parameters to AlexNet is assigned,and a correlation analysis is performed between the RDM(representational dissimilarity matrix)of its neuron re-sponses to multi-class image stimuli from ImageNet and that of the original AlexNet.It is found that the corre-lation between these two RDMs is significant.Secondly,considering that the convolution operation at each DNN layer can be regarded as a process of weighting and then summing,as well as the central limit theorem“the sum of a large number of random variables is approximately Gaussian”,the fitness between the distribution of the neuron responses and the real Gauss distribution under the same inputs is further calculated,and the correlation of the fitness between the original and random AlexNet is analyzed.Extensive experimental re-sults show that only the correlation of the fitness using real samples expresses significance.Finally,the high-layer responses from the random AlexNet is used to perform KNN(K-nearest neighbor)classification,and it is found that its classification accuracy is higher than that by KNN with the original images.Hence,similar to the reports in literature,the results re-demonstrate that a random DNN indeed has a classification ability to some extent.