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Status: Unchecked ×Keyword: L1 norm ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Improving Lightweight AdderNet via Distillation From ℓ2 to ℓ1-norm

   Dong, Chen, Wang and Xu · IEEE Transactions on Image Processing · 2023

   The paper presents Norm-Guided Distillation, which helps ℓ1-norm adder networks learn from ℓ2-norm adder networks, and reports gains on several benchmarks for lightweight networks.

   Unchecked2 claims
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   1. UncheckedThe authors' distillation method raises adder network accuracy by 10.43% (0.25× GhostNet, CIFAR-100) and 3.1% (1.0× GhostNet, ImageNet).“For example, our method improves ANN by 10.43% with 0.25× GhostNet on CIFAR-100 and 3.1% with 1.0× GhostNet on ImageNet.”
   2. UncheckedThe paper modifies the roughly estimated gradients in adder networks so they shift step by step from l2-norm to l1-norm, aiming for more accurate optimisation.“Furthermore, the roughly estimated gradients in vanilla ANNs are modified to a progressive approximation from l 2 -norm to l 1 -norm so that a more accurate optimization can be achieved.”