Encoder-DecoderwithAtrous Separable Convolution for ...
Encoder-Decoder with Atrous SeparableConvolution for Semantic Image SegmentationLiang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, andHartwig AdamGoogle Inc.{lcchen, yukun, gpapan, fschroff, pyramid pooling module or encode-decoder structureare used in deep neural networks for semantic segmentation networks are able to encode multi-scale contextual information byprobing the incoming features with filters or pooling operations at mul-tiple rates and multiple effective fields-of-view, while the latter networkscan capture sharper object boundaries by gradually recovering the spatialinformation. In this work, we propose to combine the advantages fromboth methods.}
Depthwise separable convolution: Depthwise separable convolution, fac-torizing a standard convolution into a depthwiseconvolutionfollowed by a point-wiseconvolution (i.e., 1×1 convolution), drastically reduces computation com-plexity. Specifically, the depthwise convolution performs a spatial convolution
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