Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout

Kun Wan, Boyuan Feng, Lingwei Xie, Yufei Ding

Abstract: Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNet becomes one of the most popular CNN models due to its effectiveness in feature-reuse. However, like other CNN models, DenseNets also face overfitting problem if not severer. Existing dropout method can be applied but not as effective due to the introduced nonlinear connections. In particular, the property of feature-reuse in DenseNet will be impeded, and the dropout effect will be weakened by the spatial correlation inside feature maps. To address these problems, we craft the design of a specialized dropout method from three aspects, dropout location, dropout granularity, and dropout probability. The insights attained here could potentially be applied as a general approach for boosting the accuracy of other CNN models with similar nonlinear connections. Experimental results show that DenseNets with our specialized dropout method yield better accuracy compared to vanilla DenseNet and state-of-the-art CNN models, and such accuracy boost increases with the model depth.

Keywords: Specialized dropout, computer vision

TL;DR: Realizing the drawbacks when applying original dropout on DenseNet, we craft the design of dropout method from three aspects, the idea of which could also be applied on other CNN models.