Lightweight Design and Optimization methods for DCNNs: Progress and Futures | Paper Citation Record

Observation 86672c99-16e4-465d-b7fe-59f769b62ed9 · outbound

This paper cites Designing Neural Network Architectures using Reinforcement Learning.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Designing Neural Network Architectures using Reinforcement Learning

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This paper cites Neural optimizer search with reinforcement learning, in: Precup, D., Teh, Y.W.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Neural optimizer search with reinforcement learning, in: Precup, D., Teh, Y.W

Reference 2

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This paper cites Modelcompression,in:Proceedingsofthe12thACMSIGKDDInternationalConference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Modelcompression,in:Proceedingsofthe12thACMSIGKDDInternationalConference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA

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This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

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This paper cites RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

Reference 7

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This paper cites Darkrank: Accelerating deep metric learning via cross sample similarities transfer.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Darkrank: Accelerating deep metric learning via cross sample similarities transfer

Reference 8

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This paper cites Sobolev training for neural networks, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Sobolev training for neural networks, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA

Reference 9

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

Reference 10

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This paper cites Repvgg:Makingvgg-styleconvnetsgreatagain,in:2021IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Repvgg:Makingvgg-styleconvnetsgreatagain,in:2021IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 11

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This paper cites Ghostnet: More features from cheap operations, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Ghostnet: More features from cheap operations, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 12

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Observation c8acbaf5-2f2d-4168-a76b-b650f0cb6902 · outbound

This paper cites Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, in: International Conference on Learning Representations.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, in: International Conference on Learning Representations

Reference 13

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This paper cites Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 14

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

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This paper cites Distilling the Knowledge in a Neural Network.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Distilling the Knowledge in a Neural Network

Reference 16

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This paper cites Searchingformobilenetv3,in:2019IEEE/CVFInternationalConferenceonComputerVision(ICCV),pp.1314–1324.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Searchingformobilenetv3,in:2019IEEE/CVFInternationalConferenceonComputerVision(ICCV),pp.1314–1324

Reference 17

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Observation eb11ef69-2689-42b8-b4f7-a4a579e441d7 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

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This paper cites A novel channel pruning method for deep neural network compression.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures A novel channel pruning method for deep neural network compression

Reference 19

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Observation c56d6a00-316f-4cd4-9eb2-51d691c4a179 · outbound

This paper cites Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 20

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This paper cites Binarized neural networks, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Binarized neural networks, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA

Reference 21

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This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 22

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This paper cites Compression of deep convolutional neural networks for fast and low power mobile applications, in: International Conference on Learning Representations.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Compression of deep convolutional neural networks for fast and low power mobile applications, in: International Conference on Learning Representations

Reference 23

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

Reference 24

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This paper cites Speeding-up convolutional neural networks using fine-tuned cp- decomposition, in: International Conference on Learning Representations.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Speeding-up convolutional neural networks using fine-tuned cp- decomposition, in: International Conference on Learning Representations

Reference 25

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This paper cites Gradient-based learning applied to document recognition.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Gradient-based learning applied to document recognition

Reference 26

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This paper cites Optimal brain damage, in: Touretzky, D.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Optimal brain damage, in: Touretzky, D

Reference 27

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This paper cites Memory-efficient patch-based inference for tiny deep learning, in: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Memory-efficient patch-based inference for tiny deep learning, in: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W

Reference 28

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

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This paper cites Ternary weight networks, in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Ternary weight networks, in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

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This paper cites Hierarchical Representations for Efficient Architecture Search.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Hierarchical Representations for Efficient Architecture Search

Reference 31

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This paper cites DARTS: Differentiable Architecture Search.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures DARTS: Differentiable Architecture Search

Reference 32

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This paper cites An Entropy-based Pruning Method for CNN Compression.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures An Entropy-based Pruning Method for CNN Compression

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This paper cites Extremenetworkcompressionviafiltergroupapproximation,in:Ferrari,V.,Hebert, M., Sminchisescu, C., Weiss, Y.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Extremenetworkcompressionviafiltergroupapproximation,in:Ferrari,V.,Hebert, M., Sminchisescu, C., Weiss, Y

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This paper cites Largekernelmatters—improvesemanticsegmentationbyglobalconvolutionalnetwork, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Largekernelmatters—improvesemanticsegmentationbyglobalconvolutionalnetwork, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

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This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks, in: Leibe, B., Matas, J., Sebe, N., Welling, M.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Xnor-net: Imagenet classification using binary convolutional neural networks, in: Leibe, B., Matas, J., Sebe, N., Welling, M

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This paper cites AutoML-zero: Evolving machine learning algorithms from scratch, in: III, H.D., Singh, A.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures AutoML-zero: Evolving machine learning algorithms from scratch, in: III, H.D., Singh, A

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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Mobilenetv2: Inverted residuals and linear bottlenecks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

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This paper cites Efficient acceleration of deep learning inference on resource-constrained edge devices: A review.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Efficient acceleration of deep learning inference on resource-constrained edge devices: A review

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This paper cites Very deep convolutional networks for large-scale image recognition, in: International Conference on Learning Representations, p.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Very deep convolutional networks for large-scale image recognition, in: International Conference on Learning Representations, p

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This paper cites Addersr: Towards energy efficient image super-resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Addersr: Towards energy efficient image super-resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

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This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI Press.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Inception-v4, inception-resnet and the impact of residual connections on learning, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI Press

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This paper cites Going deeper with convolutions,in:2015IEEEConferenceonComputerVisionandPatternRecognition(CVPR),pp.1–9.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Going deeper with convolutions,in:2015IEEEConferenceonComputerVisionandPatternRecognition(CVPR),pp.1–9

Reference 43

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

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This paper cites Rethinking the inception architecture for computer vision, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Rethinking the inception architecture for computer vision, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

Reference 46

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This paper cites Image classification for content-based indexing.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Image classification for content-based indexing

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This paper cites Acceleratingconvolutionalneuralnetworksformobileapplications,in:Proceedingsofthe24thACMInternational Conferenceon Multimedia,AssociationforComputingMachinery, NewYork,NY,USA.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Acceleratingconvolutionalneuralnetworksformobileapplications,in:Proceedingsofthe24thACMInternational Conferenceon Multimedia,AssociationforComputingMachinery, NewYork,NY,USA

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

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This paper cites Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

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This paper cites Designing energy-efficient convolutional neural networks using energy-aware pruning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Designing energy-efficient convolutional neural networks using energy-aware pruning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 52

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This paper cites A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 53

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Accelerating very deep convolutional networks for classification and detection

Reference 54

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Efficientandaccurateapproximationsofnonlinearconvolutionalnetworks,in:2015IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 55

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This paper cites Object detection with deep learning: A review.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Object detection with deep learning: A review

Reference 56

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures ProceedingsoftheAAAIConferenceonArtificialIntelligence32

Reference 57

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Lightweight Design and Optimization methods for DCNNs: Progress and Futures Unresolved cited work

Reference 58

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This paper cites Learning transferable architectures for scalable image recognition, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Lightweight Design and Optimization methods for DCNNs: Progress and Futures Learning transferable architectures for scalable image recognition, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 59

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