Unsupervised Learning of Initialization in Deep Neural Networks via Maximum Mean Discrepancy

Despite the recent success of stochastic gradient descent in deep learning, it is often difficult to train a deep neural network with an inappropriate choice of its initial parameters. Even if training is successful, it has been known that the initial parameter configuration may negatively impact generalization. In this paper, we propose an unsupervised algorithm to find good initialization for input data, given that a downstream task is d-way classification. We first notice that each parameter configuration in the parameter space corresponds to one particular downstream task of d-way classification. We then conjecture that the success of learning is directly related to how diverse downstream tasks are in the vicinity of the initial parameters. We thus design an algorithm that encourages small perturbation to the initial parameter configuration leads to a diverse set of d-way classification tasks. In other words, the proposed algorithm ensures a solution to any downstream task to be near the initial parameter configuration. We empirically evaluate the proposed algorithm on various tasks derived from MNIST with a fully connected network. In these experiments, we observe that our algorithm improves average test accuracy across most of these tasks, and that such improvement is greater when the number of labelled examples is small.