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1,353 claims from 842 papers are on the record. 46 have been checked so far; the other 1,307 have no check with a result yet.

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Keyword: MobileNet ×Clear all

5 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

   Tan and Le · arXiv (Cornell University) · 2019

   The paper studies how to scale convolutional networks by balancing depth, width and resolution, and uses this to build EfficientNets that report better accuracy and efficiency than earlier ConvNets.

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   1. UncheckedThe paper's EfficientNet-B7 reaches 84.3% top-1 accuracy on ImageNet, being 8.4x smaller and 6.1x faster at inference than the best existing ConvNet.“In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet.”

2. Computer Science › Advanced Neural Network Applications

   AMC: AutoML for Model Compression and Acceleration on Mobile Devices

   Yihui, Lin, Liu, Wang, Li and Han · arXiv (Cornell University) · 2018

   The authors use reinforcement learning to automatically choose how to compress neural networks for mobile devices, reporting better accuracy and speed than rule-based, hand-designed compression policies.

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   1. UncheckedAt a 4x cut in computation, the paper's automated compression of VGG-16 on ImageNet reached 2.7% better accuracy than a handcrafted compression policy.“Under 4x FLOPs reduction, we achieved 2.7% better accuracy than the handcrafted model compression policy for VGG-16 on ImageNet.”
   2. UncheckedApplied to MobileNet, the automated AMC compression method sped up measured inference 1.81x on an Android phone and 1.43x on a Titan XP GPU, losing 0.1% accuracy.“We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81x speedup of measured inference latency on an Android phone and 1.43x speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.”

3. Computer Science › Advanced Neural Network Applications

   MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

   Liu, Mu, Zhang et al. · arXiv (Cornell University) · 2019

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   1. Unchecked“Compared to the state-of-the-art pruning methods, we have demonstrated superior performances on MobileNet V1/V2 and ResNet.”
   2. Unchecked“The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time.”