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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Yifan Liu
- sl:arxiv_num : 1903.04197
- sl:arxiv_published : 2019-03-11T10:05:09Z
- sl:arxiv_summary : In this paper, we consider transferring the structure information from large
networks to small ones for dense prediction tasks. Previous knowledge
distillation strategies used for dense prediction tasks often directly borrow
the distillation scheme for image classification and perform knowledge
distillation for each pixel separately, leading to sub-optimal performance.
Here we propose to distill structured knowledge from large networks to small
networks, taking into account the fact that dense prediction is a structured
prediction problem. Specifically, we study two structured distillation schemes:
i)pair-wise distillation that distills the pairwise similarities by building a
static graph, and ii)holistic distillation that uses adversarial training to
distill holistic knowledge. The effectiveness of our knowledge distillation
approaches is demonstrated by extensive experiments on three dense prediction
tasks: semantic segmentation, depth estimation, and object detection.@en
- sl:arxiv_title : Structured Knowledge Distillation for Dense Prediction@en
- sl:arxiv_updated : 2020-02-20T23:52:50Z
- sl:bookmarkOf : https://arxiv.org/abs/1903.04197
- sl:creationDate : 2020-04-16
- sl:creationTime : 2020-04-16T14:13:03Z
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