Samsung Advanced Institute of Technology

In this paper, we propose a Generative Translation Classification Network (GTCN) for
improving visual classification accuracy in settings where classes are visually similar and data is scarce.
For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic
translation network end-to-end. The translation network is used to perform on-line data augmentation
across classes, whereas previous works have mostly involved domain adaptation. To help the model
further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss.
We perform experiments on multiple datasets to demonstrate the proposed method’s performance in varied
settings. Of particular interest, training on 40％ of the dataset is enough for our model to surpass the
performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we
achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.