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This paper introduces a concept of layer aggregation to describe howinformation from previous layers can be reused to better extract features atthe current layer. While DenseNet is a typical example of the layer aggregationmechanism, its redundancy has been commonly criticized in the literature. Thismotivates us to propose a very light-weighted module, called recurrent layeraggregation (RLA), by making use of the sequential structure of layers in adeep CNN. Our RLA module is compatible with many mainstream deep CNNs,including ResNets, Xception and MobileNetV2, and its effectiveness is verifiedby our extensive experiments on image classification, object detection andinstance segmentation tasks. Specifically, improvements can be uniformlyobserved on CIFAR, ImageNet and MS COCO datasets, and the correspondingRLA-Nets can surprisingly boost the performances by 2-3% on the objectdetection task. This evidences the power of our RLA module in helping main CNNsbetter learn structural information in images.

Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation