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1,320 claims from 825 papers are on the record. 46 have been checked so far; the other 1,274 have no check with a result yet.

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Keyword: ImageNet ×Clear all

9 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 › Image Retrieval and Classification Techniques

   ImageNet: A large-scale hierarchical image database

   Deng, Dong, Socher, Li, Li and Fei-Fei · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2009

   The paper introduces ImageNet, a large image database organised by the WordNet hierarchy, describes how it was built with Amazon Mechanical Turk, and illustrates its usefulness with three simple applications.

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   1. UncheckedThe authors say ImageNet is much larger, more diverse and more accurate than the image datasets available when the paper was written.“We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets.”

3. Computer Science › Advanced Neural Network Applications

   Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

   Ioffe and Szegedy · arXiv (Cornell University) · 2015

   The paper introduces Batch Normalization, which normalizes layer inputs within each training mini-batch to speed up deep network training and improve image classification accuracy on ImageNet.

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   1. UncheckedThe authors say Batch Normalization lets neural networks be trained with much higher learning rates and with less care over how the starting parameters are set.“Batch Normalization allows us to use much higher learning rates and be less careful about initialization.”

4. Computer Science › Advanced Neural Network Applications

   Going deeper with Image Transformers

   Touvron, Cord, Sablayrolles, Synnaeve and Jeǵou · IEEE/CVF International Conference on Computer Vision (ICCV) · 2021

   The authors build and optimise deeper image transformers, with two architecture changes that let accuracy keep improving with depth, reaching 86.5% top-1 on ImageNet with no external data.

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   1. UncheckedThe authors' best image transformer sets a new state of the art on ImageNet Reassessed labels and ImageNet-V2 (match frequency), without extra training data.“Moreover, our best model establishes the new state of the art on Imagenet with Reassessed labels and Imagenet-V2 / match frequency, in the setting with no additional training data.”

5. Computer Science › Advanced Neural Network Applications

   Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

   He, Zhang, Ren and Sun · arXiv (Cornell University) · 2015

   The paper proposes PReLU, a generalised rectifier, and a initialisation method for rectifier networks, reaching 4.94% top-5 error on ImageNet 2012, which it says is the first result to surpass human-level performance.

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   1. UncheckedThe authors say PReLU, a adjustable rectifier unit, improves how well a network fits its data at almost no extra computing cost and with little risk of overfitting.“PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk.”
   2. UncheckedA new initialisation method designed for rectifier units lets very deep rectified networks be trained from scratch, and allows deeper or wider designs to be explored.“This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures.”
   3. UncheckedUsing PReLU networks, the authors report a 4.94% top-5 test error on the ImageNet 2012 image classification dataset.“Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset.”

6. Computer Science › Advanced Neural Network Applications

   PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator

   Wang, Xie, Liu, Zhang and Cheng · arXiv (Cornell University) · 2024

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   1. Unchecked“For example, under 300M FLOPs constraint, our pruned MobileNetV2 achieves 75.2% Top-1 accuracy on ImageNet dataset, exceeding the original MobileNetV2 by 2.6 units while only cost 30%/16% times than BCNet/AutoAlim.”