  These days, although deep neural networks (DNNs) have achieved a noticeable
progress in a wide range of research area, it lacks the adaptability to be
employed in the real-world applications because of the environment discrepancy
problem. Such a problem originates from the difference between training and
testing environments, and it is widely known that it causes serious performance
degradation, when a pretrained DNN model is applied to a new testing
environment. Therefore, in this paper, we introduce a novel approach that
allows end-users to exploit pretrained DNN models in their own testing
environment without modifying the models. To this end, we present a
\textit{meta input} which is an additional input transforming the distribution
of testing data to be aligned with that of training data. The proposed meta
input can be optimized with a small number of testing data only by considering
the relation between testing input data and its output prediction. Also, it
does not require any knowledge of the network's internal architecture and
modification of its weight parameters. Then, the obtained meta input is added
to testing data in order to shift the distribution of testing data to that of
originally used training data. As a result, end-users can exploit well-trained
models in their own testing environment which can differ from the training
environment. We validate the effectiveness and versatility of the proposed meta
input by showing the robustness against the environment discrepancy through the
comprehensive experiments with various tasks.
