  Imbalanced datasets widely exist in practice and area great challenge for
training deep neural models with agood generalization on infrequent classes. In
this work, wepropose a new rare-class sample generator (RSG) to solvethis
problem. RSG aims to generate some new samplesfor rare classes during training,
and it has in particularthe following advantages: (1) it is convenient to use
andhighly versatile, because it can be easily integrated intoany kind of
convolutional neural network, and it works wellwhen combined with different
loss functions, and (2) it isonly used during the training phase, and
therefore, no ad-ditional burden is imposed on deep neural networks duringthe
testing phase. In extensive experimental evaluations, weverify the
effectiveness of RSG. Furthermore, by leveragingRSG, we obtain competitive
results on Imbalanced CIFARand new state-of-the-art results on Places-LT,
ImageNet-LT, and iNaturalist 2018. The source code is available at
https://github.com/Jianf-Wang/RSG.
