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1,706 claims from 1,050 papers are on the record. 46 have been checked so far; the other 1,660 have no check with a result yet.

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Status: Unchecked ×Keyword: deep convolutional neural networks ×Clear all

11 claims from 6 papers

1. Computer Science › Advanced Neural Network Applications

   ImageNet classification with deep convolutional neural networks

   Krizhevsky, Sutskever and Hinton · Communications of the ACM · 2017

   The authors trained a large deep convolutional neural network on 1.2 million ImageNet images across 1000 classes and report test error rates well below earlier methods, plus a winning entry in 2012.

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   1. UncheckedA large deep convolutional network classified ImageNet LSVRC-2010 test images with 37.5% top-1 and 17.0% top-5 error, which the authors say beat prior results.“On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art.”
   2. UncheckedA variant of the authors' deep neural network won the ILSVRC-2012 competition with a top-5 test error of 15.3%, against 26.2% for the runner-up.“We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.”

2. Computer Science › Advanced Neural Network Applications

   Going Deeper with Convolutions

   Szegedy, Liu, Jia et al. · arXiv (Cornell University) · 2014

   The paper proposes Inception, a deep convolutional network that set the ILSVRC 2014 state of the art; its 22-layer form, GoogLeNet, is assessed for classification and detection.

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   1. UncheckedThe Inception design lets a network grow deeper and wider without increasing the amount of computing it needs, according to the paper.“This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

   Weyn, Durran and Caruana · Journal of Advances in Modeling Earth Systems · 2020

   The authors present an improved neural network that forecasts global weather on a cubed-sphere grid, stays stable over long runs, and is faster though less accurate than operational forecasting models.

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   1. UncheckedFor short- to medium-range forecasts, the authors' neural network model beat persistence, climatology and a coarse-resolution physics-based weather model.“For short- to medium-range forecasting, our model significantly outperforms persistence, climatology, and a coarse-resolution dynamical numerical weather prediction (NWP) model.”
   2. Unchecked“On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top-of-atmosphere solar forcing.”

4. Computer Science › Advanced Neural Network Applications

   Residual Attention Network for Image Classification

   Wang, Jiang, Qian et al. · arXiv (Cornell University) · 2017

   The paper proposes the Residual Attention Network, a deep image-classification network built from stacked attention modules, and reports strong results on three benchmark datasets.

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   1. UncheckedThe Residual Attention Network reports state-of-the-art error rates on CIFAR-10 (3.90%), CIFAR-100 (20.45%) and ImageNet (4.8% top-5, single model, single crop).“Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error).”
   2. Unchecked“Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200.”
   3. UncheckedThe authors report that their Residual Attention Network keeps working well when some of the training labels are noisy, meaning wrong.“The experiment also demonstrates that our network is robust against noisy labels.”

5. Computer Science › Advanced Neural Network Applications

   Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration

   He, Ding, Liu, Zhu, Zhang and Yang · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2020

   The paper proposes LFPC, a method that learns which filter pruning criteria to use for each layer, and tests it on three image classification benchmarks.

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   1. UncheckedOn ILSVRC-2012, the LFPC pruning method cuts over 60% of ResNet-50's FLOPs while losing only 0.83% top-5 accuracy.“Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.”

6. Computer Science › Advanced Neural Network Applications

   Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

   He, Liu, Wang, Hu and Yang · arXiv (Cornell University) · 2018

   The paper proposes FPGM, a method that prunes redundant filters in convolutional neural networks using the geometric median, and tests it on two image classification benchmarks.

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   1. UncheckedOn CIFAR-10, the FPGM pruning method cuts over 52% of the computation in ResNet-110 while giving a 2.69% relative accuracy improvement.“Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement.”
   2. UncheckedOn ILSVRC-2012, the FPGM pruning method cuts over 42% of ResNet-101's FLOPs with no drop in top-5 accuracy, which the authors say advances the state of the art.“Moreover, on ILSVRC-2012, FPGM reduces more than 42% FLOPs on ResNet-101 without top-5 accuracy drop, which has advanced the state-of-the-art.”