Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Compact Single Image Super-Resolution via Contrastive
  Self-distillation   Convolutional neural networks (CNNs) are highly successful for
super-resolution (SR) but often require sophisticated architectures with heavy
memory cost and computational overhead, significantly restricts their practical
deployments on resource-limited devices. In this paper, we proposed a novel
contrastive self-distillation (CSD) framework to simultaneously compress and
accelerate various off-the-shelf SR models. In particular, a channel-splitting
super-resolution network can first be constructed from a target teacher network
as a compact student network. Then, we propose a novel contrastive loss to
improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer.
Extensive experiments demonstrate that the proposed CSD scheme effectively
compresses and accelerates several standard SR models such as EDSR, RCAN and
CARN. Code is available at https://github.com/Booooooooooo/CSD.

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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph