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Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
[article]
2020
arXiv
pre-print
This work aims to enable on-device training of convolutional neural networks (CNNs) by reducing the computation cost at training time. CNN models are usually trained on high-performance computers and only the trained models are deployed to edge devices. But the statically trained model cannot adapt dynamically in a real environment and may result in low accuracy for new inputs. On-device training by learning from the real-world data after deployment can greatly improve accuracy. However, the
arXiv:2007.03213v1
fatcat:ecbaiqlalbcy3j6tznrkrksd54