Dual Path Networks
In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64 × 4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.
The authors propose a new network architecture which is a combination of ResNets and DenseNets. They introduce a very informative theoretical formulation which can be used to formulate ResNets, DenseNets and their proposed architecture. The authors present compelling results, analysis and statistics on compelling benchmarks.
Pros:
(+) The paper is well written with theoretical and empirical results
(+) The authors provide useful analysis and statistics
(+) The impact of DPNs is shown on a variety of computer vision tasks
(+) The performance of the DPNs on the presented vision tasks is compelling
Cons:
(-) Optional results on MS COCO would make the paper even stronger
Network engineering is an important field and it is important that it is done correctly, with analysis and many in depth experiments. The impact of new architectures comes through their generalization capabilities. This paper does a good job on all of the above. Even though there is no groundbreaking novelty in the proposed micro-blocks, the authors provide extensive analysis and statistics for their proposed model. They also present results on a variety of computer vision tasks which makes their paper complete. Especially the analysis of memory usage, training speed and number of flops is very revealing and informative. 
The authors present results on compelling computer vision tasks, such as ImageNet, Places365 and PASCAL VOC. These are traditionally the most difficult datasets and tasks in computer vision. It would make the paper even stronger if the authors presented results on MS COCO, which is harder than PASCAL VOC.