  Large-scale deep neural networks (DNNs) such as convolutional neural networks
(CNNs) have achieved impressive performance in audio classification for their
powerful capacity and strong generalization ability. However, when training a
DNN model on low-resource tasks, it is usually prone to overfitting the small
data and learning too much redundant information. To address this issue, we
propose to use variational information bottleneck (VIB) to mitigate overfitting
and suppress irrelevant information. In this work, we conduct experiments ona
4-layer CNN. However, the VIB framework is ready-to-use and could be easily
utilized with many other state-of-the-art network architectures. Evaluation on
a few audio datasets shows that our approach significantly outperforms baseline
methods, yielding more than 5.0% improvement in terms of classification
accuracy in some low-source settings.
