Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
This work addresses overfitting in DenseNet for visual recognition, offering a potential general approach for CNNs with nonlinear connections, but it is incremental as it builds on existing dropout techniques.
The paper tackles overfitting in DenseNet by designing a specialized dropout method that addresses feature-reuse and spatial correlation issues, resulting in improved accuracy over vanilla DenseNet and state-of-the-art models, with gains increasing with model depth.
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.