  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.
