In this paper, we present a novel deep learning approach, deeply-fused nets.
The central idea of our approach is deep fusion, i.e., combine the intermediate
representations of base networks, where the fused output serves as the input of
the remaining part of each base network, and perform such combinations deeply
over several intermediate representations. The resulting deeply fused net
enjoys several benefits. First, it is able to learn multi-scale representations
as it enjoys the benefits of more base networks, which could form the same
fused network, other than the initial group of base networks. Second, in our
suggested fused net formed by one deep and one shallow base networks, the flows
of the information from the earlier intermediate layer of the deep base network
to the output and from the input to the later intermediate layer of the deep
base network are both improved. Last, the deep and shallow base networks are
jointly learnt and can benefit from each other. More interestingly, the
essential depth of a fused net composed from a deep base network and a shallow
base network is reduced because the fused net could be composed from a less
deep base network, and thus training the fused net is less difficult than
training the initial deep base network. Empirical results demonstrate that our
approach achieves superior performance over two closely-related methods, ResNet
and Highway, and competitive performance compared to the state-of-the-arts.

本文介绍了一种深度融合的神经网络，能够学习多尺度表现并由浅层网络和深层网络联合学习，提出的网络相比 ResNet 和 Highway 等方法具有更好的性能表现。

深度融合网络

Deeply-Fused Nets

It is well known that contextual and multi-scale representations are
important for accurate visual recognition. In this paper we present the
Inside-Outside Net (ION), an object detector that exploits information both
inside and outside the region of interest. Contextual information outside the
region of interest is integrated using spatial recurrent neural networks.
Inside, we use skip pooling to extract information at multiple scales and
levels of abstraction. Through extensive experiments we evaluate the design
space and provide readers with an overview of what tricks of the trade are
important. ION improves state-of-the-art on PASCAL VOC 2012 object detection
from 73.9% to 76.4% mAP. On the new and more challenging MS COCO dataset, we
improve state-of-art-the from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection
Challenge, our ION model won the Best Student Entry and finished 3rd place
overall. As intuition suggests, our detection results provide strong evidence
that context and multi-scale representations improve small object detection.

本文介绍了使用 Inside-Outside Net（ION）实现的物体检测器，它在感兴趣区域内外分别利用信息及多尺度表示，并结合了空间循环神经网络依靠感兴趣区域外的语境信息。该方法在 PASCAL VOC 2012 和 MS COCO 等数据集上均取得了优于当时最优结果的表现，并提供了通用的设计原则和技巧，结果表明多尺度表示和上下文信息对小物体检测有明显的改善。