Transcription of Structured Knowledge Distillation for Semantic Segmentation
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Structured Knowledge Distillation for Semantic Segmentation
Structured Knowledge Distillation for Semantic SegmentationYifan Liu1 Ke Chen2 Chris Liu2 Zengchang Qin3,4 Zhenbo Luo5 Jingdong Wang2 1 The University of Adelaide2 Microsoft Research Asia3 Beihang University4 Keep Labs, Keep Research ChinaAbstractIn this paper, we investigate the Knowledge distillationstrategy for training small Semantic Segmentation networksby making use of large networks. We start from the straight-forward scheme, pixel-wise Distillation , which applies thedistillation scheme adopted for image classi cation andperforms Knowledge Distillation for each further propose to distill thestructuredknowledge fromlarge networks to small networks, which is motivated by thatsemantic Segmentation is a Structured prediction study two Structured Distillation schemes: (i)pair-wisedistillation that distills the pairwise similarities, and (ii)holisticdistillation that uses GAN to distill holistic knowl-edge.
DeepLab [5, 6, 7, 48], PSPNet [56], OCNet [50], Re-fineNet [23] and DenseASPP [46] have achieved significant improvement in segmentation accuracy, often with cumber-some models and expensive computation. Recently, neural networks with small model size, light computation cost and high segmentation accuracy, have at-
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