  Recently, several optimization methods have been successfully applied to the
hyperparameter optimization of deep neural networks (DNNs). The methods work by
modeling the joint distribution of hyperparameter values and corresponding
error. Those methods become less practical when applied to modern DNNs whose
training may take a few days and thus one cannot collect sufficient
observations to accurately model the distribution. To address this challenging
issue, we propose a method that learns to transfer optimal hyperparameter
values for a small source dataset to hyperparameter values with comparable
performance on a dataset of interest. As opposed to existing transfer learning
methods, our proposed method does not use hand-designed features. Instead, it
uses surrogates to model the hyperparameter-error distributions of the two
datasets and trains a neural network to learn the transfer function. Extensive
experiments on three CV benchmark datasets clearly demonstrate the efficiency
of our method.
