  Both accuracy and efficiency are of significant importance to the task of
semantic segmentation. Existing deep FCNs suffer from heavy computations due to
a series of high-resolution feature maps for preserving the detailed knowledge
in dense estimation. Although reducing the feature map resolution (i.e.,
applying a large overall stride) via subsampling operations (e.g., pooling and
convolution striding) can instantly increase the efficiency, it dramatically
decreases the estimation accuracy. To tackle this dilemma, we propose a
knowledge distillation method tailored for semantic segmentation to improve the
performance of the compact FCNs with large overall stride. To handle the
inconsistency between the features of the student and teacher network, we
optimize the feature similarity in a transferred latent domain formulated by
utilizing a pre-trained autoencoder. Moreover, an affinity distillation module
is proposed to capture the long-range dependency by calculating the non-local
interactions across the whole image. To validate the effectiveness of our
proposed method, extensive experiments have been conducted on three popular
benchmarks: Pascal VOC, Cityscapes and Pascal Context. Built upon a highly
competitive baseline, our proposed method can improve the performance of a
student network by 2.5\% (mIOU boosts from 70.2 to 72.7 on the cityscapes test
set) and can train a better compact model with only 8\% float operations
(FLOPS) of a model that achieves comparable performances.
