  It is widely known that very small datasets produce overfitting in Deep
Neural Networks (DNNs), i.e., the network becomes highly biased to the data it
has been trained on. This issue is often alleviated using transfer learning,
regularization techniques and/or data augmentation. This work presents a new
approach, independent but complementary to the previous mentioned techniques,
for improving the generalization of DNNs on very small datasets in which the
involved classes share many visual features. The proposed methodology, called
FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs,
creates as many generators as classes in the problem. Each generator, $k$,
learns the transformations that bring the input image into the k-class domain.
We introduce a classification loss in the generators to drive the leaning of
specific k-class transformations. Our experiments demonstrate that the proposed
transformations improve the generalization of the classification model in three
diverse datasets.
