基于信息修正的深度残差学习

Deep Residual Learning with Information Refinement

提出了一种新的深度残差网络的拓展模块,有效提高了学习表示的鲁棒性。所提出的方法是一个简单的即插即用模块,即组卷积式编码-解码结构,它可以作为一个额外的信息过滤部件集成到原来的深度残差网络中。利用编码器的下采样来产生信息压缩过的特征图,解码器模块被驱动以产生激活准确的特征图,其能够突出显示输入图片中最具有判别力的区域,最后通过元素级相加和激活操作对输入特征进行信息修正。为了使设计的模型计算更加高效,通过减少残差分支的通道数来探究其轻量级版本的表现,发现并没有明显的性能下降现象。在各种基于残差网络的架构上进行实验,获得了一致性的性能提高,而且付出的计算代价与原始版本相比差别不大,甚至还低。

A novel extension of residual learning is presented for deep networks which effectively improves the robustness of the learned representation.The method integrates a plug-and-play module,that is,a grouped convolutional encoder-decoder,as additional shortcuts to the original residual architecture.Due to the down-sampling in encoder stage,the decoder modules are driven to produce focally activated feature maps,which highlights the most discriminative regions of input images,and imposes local enhancement on input features through element-wise addition.For efficient model design,we exploit lightweight counterparts by removing part channels of residual mappings,without showing obvious performance degradation.We obtain consistent accuracy gain for various residual architectures with comparable or even lower model complexity.