Unsupervised Knowledge Transfer Using Similarity Embeddings.

Abstract

With the advent of deep neural networks, there is a growing interest in transferring the knowledge from a large and complex model to a smaller and faster one. In this brief, a method for unsupervised knowledge transfer (KT) between neural networks is proposed. To the best of our knowledge, the proposed method is the first method that utilizes similarity-induced embeddings to transfer the knowledge between any two layers of neural networks, regardless of the number of neurons in each of them. By this way, the knowledge is transferred without using any lossy dimensionality reduction transformations or requiring any information about the complex model, except for the activations of the layer used for KT. This is in contrast with most existing approaches that only generate soft-targets for training the smaller neural network or directly use the weights of the larger model. The proposed method is evaluated using six image data sets and it is demonstrated, through extensive experiments, that the knowledge of a neural network can be successfully transferred using different kinds of (synthetic or not) data, ranging from cross-domain data to just randomly generated data.