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1,212 claims from 763 papers are on the record. 44 have been checked so far; the other 1,168 have no check with a result yet.

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Keyword: CIFAR-100 ×Clear all

6 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   Learning Multiple Layers of Features from Tiny Images

   Krizhevsky · 2024

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   1. Unchecked“We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex.”

2. Computer Science › Advanced Neural Network Applications

   GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer

   Lin, Wang and Lin · PeerJ Computer Science · 2024

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   1. Unchecked“The experimental results reveal that the accuracy rate only drops by 6.58% when the channel pruning rate is 89% for VGG-19/CIFAR-100.”
   2. Unchecked“In addition, the lightweight model inference speed is 9.10 times faster than that of the original large model.”

3. Computer Science › Advanced Neural Network Applications

   Spending Your Winning Lottery Better After Drawing It

   Jaiswal, Ma, Chen, Ding and Wang · arXiv (Cornell University) · 2021

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   1. Unchecked“Instead, by plugging in purposeful "tweaks" of the sparse subnetwork architecture or its training recipe, its retraining can be significantly improved than the default, especially at high sparsity levels.”
   2. Unchecked“Specifically, we have achieved a significant and consistent performance gain of1.05% - 4.93% for ResNet18 on CIFAR-100 over vanilla-LTH.”
   3. Unchecked“Moreover, our methods are shown to generalize across datasets (CIFAR10, CIFAR100, TinyImageNet) and architectures (Vgg16, ResNet-18/ResNet-34, MobileNet).”