  In general, sufficient data is essential for the better performance and
generalization of deep-learning models. However, lots of limitations(cost,
resources, etc.) of data collection leads to lack of enough data in most of the
areas. In addition, various domains of each data sources and licenses also lead
to difficulties in collection of sufficient data. This situation makes us hard
to utilize not only the pre-trained model, but also the external knowledge.
Therefore, it is important to leverage small dataset effectively for achieving
the better performance. We applied some techniques in three aspects: data, loss
function, and prediction to enable training from scratch with less data. With
these methods, we obtain high accuracy by leveraging ImageNet data which
consist of only 50 images per class. Furthermore, our model is ranked 4th in
Visual Inductive Printers for Data-Effective Computer Vision Challenge.
