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ResNet or DenseNet? Nowadays, most deep learning based approaches areimplemented with seminal backbone networks, among them the two arguably mostfamous ones are ResNet and DenseNet. Despite their competitive performance andoverwhelming popularity, inherent drawbacks exist for both of them. For ResNet,the identity shortcut that stabilizes training also limits its representationcapacity, while DenseNet has a higher capacity with multi-layer featureconcatenation. However, the dense concatenation causes a new problem ofrequiring high GPU memory and more training time. Partially due to this, it isnot a trivial choice between ResNet and DenseNet. This paper provides a unifiedperspective of dense summation to analyze them, which facilitates a betterunderstanding of their core difference. We further propose dense weightednormalized shortcuts as a solution to the dilemma between them. Our proposeddense shortcut inherits the design philosophy of simple design in ResNet andDenseNet. On several benchmark datasets, the experimental results show that theproposed DSNet achieves significantly better results than ResNet, and achievescomparable performance as DenseNet but requiring fewer computation resources.

ResNet or DenseNet? Introducing Dense Shortcuts to ResNet