Learning Filter Basis for Convolutional Neural Network Compression | Paper Citation Record

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This paper cites Tensorﬂow: A system for large-scale machine learning.

Learning Filter Basis for Convolutional Neural Network Compression Tensorﬂow: A system for large-scale machine learning

Reference 1

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This paper cites NTIRE 2017 chal- lenge on single image super-resolution: Dataset and study.

Learning Filter Basis for Convolutional Neural Network Compression NTIRE 2017 chal- lenge on single image super-resolution: Dataset and study

Reference 2

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This paper cites Learning the num- ber of neurons in deep networks.

Learning Filter Basis for Convolutional Neural Network Compression Learning the num- ber of neurons in deep networks

Reference 3

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This paper cites Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Learning Filter Basis for Convolutional Neural Network Compression Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Reference 4

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This paper cites Compressing neural networks with the hashing trick.

Learning Filter Basis for Convolutional Neural Network Compression Compressing neural networks with the hashing trick

Reference 5

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This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations.

Learning Filter Basis for Convolutional Neural Network Compression Binaryconnect: Training deep neural networks with binary weights during propagations

Reference 6

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This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Learning Filter Basis for Convolutional Neural Network Compression Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 7

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This paper cites Eco: Efﬁcient convolution opera- tors for tracking.

Learning Filter Basis for Convolutional Neural Network Compression Eco: Efﬁcient convolution opera- tors for tracking

Reference 8

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This paper cites Exploiting linear structure within con- volutional networks for efﬁcient evaluation.

Learning Filter Basis for Convolutional Neural Network Compression Exploiting linear structure within con- volutional networks for efﬁcient evaluation

Reference 9

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This paper cites Learning a deep convolutional network for image super-resolution.

Learning Filter Basis for Convolutional Neural Network Compression Learning a deep convolutional network for image super-resolution

Reference 10

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This paper cites Fast R-CNN.

Learning Filter Basis for Convolutional Neural Network Compression Fast R-CNN

Reference 11

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This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Learning Filter Basis for Convolutional Neural Network Compression Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 12

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This paper cites Multi-bin Trainable Linear Unit for Fast Image Restoration Networks.

Learning Filter Basis for Convolutional Neural Network Compression Multi-bin Trainable Linear Unit for Fast Image Restoration Networks

Reference 13

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This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Learning Filter Basis for Convolutional Neural Network Compression Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 14

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This paper cites Learning both weights and connections for efﬁcient neural network.

Learning Filter Basis for Convolutional Neural Network Compression Learning both weights and connections for efﬁcient neural network

Reference 15

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This paper cites Deep residual learning for image recognition.

Learning Filter Basis for Convolutional Neural Network Compression Deep residual learning for image recognition

Reference 16

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This paper cites AMC: AutoML for model compression and ac- celeration on mobile devices.

Learning Filter Basis for Convolutional Neural Network Compression AMC: AutoML for model compression and ac- celeration on mobile devices

Reference 17

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This paper cites Channel pruning for accelerating very deep neural networks.

Learning Filter Basis for Convolutional Neural Network Compression Channel pruning for accelerating very deep neural networks

Reference 18

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This paper cites Densely connected convolutional net- works.

Learning Filter Basis for Convolutional Neural Network Compression Densely connected convolutional net- works

Reference 19

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This paper cites Single image super-resolution from transformed self-exemplars.

Learning Filter Basis for Convolutional Neural Network Compression Single image super-resolution from transformed self-exemplars

Reference 20

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This paper cites Speeding up convolutional neural networks with low rank expansions.

Learning Filter Basis for Convolutional Neural Network Compression Speeding up convolutional neural networks with low rank expansions

Reference 21

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This paper cites Efﬁcient neural network compression.

Learning Filter Basis for Convolutional Neural Network Compression Efﬁcient neural network compression

Reference 22

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This paper cites Accurate image super-resolution using very deep convolutional net- works.

Learning Filter Basis for Convolutional Neural Network Compression Accurate image super-resolution using very deep convolutional net- works

Reference 23

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This paper cites Adam: A Method for Stochastic Optimization.

Learning Filter Basis for Convolutional Neural Network Compression Adam: A Method for Stochastic Optimization

Reference 24

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This paper cites Learning multiple layers of features from tiny images.

Learning Filter Basis for Convolutional Neural Network Compression Learning multiple layers of features from tiny images

Reference 25

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This paper cites Imagenet classiﬁcation with deep convolutional neural net- works.

Learning Filter Basis for Convolutional Neural Network Compression Imagenet classiﬁcation with deep convolutional neural net- works

Reference 26

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This paper cites Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

Learning Filter Basis for Convolutional Neural Network Compression Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 27

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This paper cites Photo- realistic single image super-resolution using a generative ad- versarial network.

Learning Filter Basis for Convolutional Neural Network Compression Photo- realistic single image super-resolution using a generative ad- versarial network

Reference 28

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This paper cites Joint blind motion deblurring and depth estimation of light ﬁeld.

Learning Filter Basis for Convolutional Neural Network Compression Joint blind motion deblurring and depth estimation of light ﬁeld

Reference 29

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This paper cites Ternary Weight Networks.

Learning Filter Basis for Convolutional Neural Network Compression Ternary Weight Networks

Reference 30

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This paper cites CARN: convolutional anchored re- gression network for fast and accurate single image super- resolution.

Learning Filter Basis for Convolutional Neural Network Compression CARN: convolutional anchored re- gression network for fast and accurate single image super- resolution

Reference 31

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This paper cites Exploiting kernel sparsity and entropy for inter- pretable CNN compression.

Learning Filter Basis for Convolutional Neural Network Compression Exploiting kernel sparsity and entropy for inter- pretable CNN compression

Reference 32

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This paper cites 3D appearance super-resolution with deep learning.

Learning Filter Basis for Convolutional Neural Network Compression 3D appearance super-resolution with deep learning

Reference 33

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This paper cites Enhanced deep residual networks for single image super-resolution.

Learning Filter Basis for Convolutional Neural Network Compression Enhanced deep residual networks for single image super-resolution

Reference 34

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This paper cites Learning efﬁcient convolutional networks through network slimming.

Learning Filter Basis for Convolutional Neural Network Compression Learning efﬁcient convolutional networks through network slimming

Reference 35

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This paper cites Fully convolutional networks for semantic segmentation.

Learning Filter Basis for Convolutional Neural Network Compression Fully convolutional networks for semantic segmentation

Reference 36

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This paper cites Martin, C.

Learning Filter Basis for Convolutional Neural Network Compression Martin, C

Reference 37

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This paper cites Cascaded projec- tion: End-to-end network compression and acceleration.

Learning Filter Basis for Convolutional Neural Network Compression Cascaded projec- tion: End-to-end network compression and acceleration

Reference 38

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This paper cites Blind image deblurring using dark channel prior.

Learning Filter Basis for Convolutional Neural Network Compression Blind image deblurring using dark channel prior

Reference 39

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This paper cites Automatic differentiation in Pytorch.

Learning Filter Basis for Convolutional Neural Network Compression Automatic differentiation in Pytorch

Reference 40

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This paper cites Extreme network compression via ﬁlter group approximation.

Learning Filter Basis for Convolutional Neural Network Compression Extreme network compression via ﬁlter group approximation

Reference 41

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This paper cites Xnor-net: Imagenet classiﬁcation using bi- nary convolutional neural networks.

Learning Filter Basis for Convolutional Neural Network Compression Xnor-net: Imagenet classiﬁcation using bi- nary convolutional neural networks

Reference 42

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Observation c81fa35b-e449-436b-9284-0c2dd02b436f · outbound

This paper cites You only look once: Uniﬁed, real-time object de- tection.

Learning Filter Basis for Convolutional Neural Network Compression You only look once: Uniﬁed, real-time object de- tection

Reference 43

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This paper cites Faster R-CNN: Towards real-time object detection with re- gion proposal networks.

Learning Filter Basis for Convolutional Neural Network Compression Faster R-CNN: Towards real-time object detection with re- gion proposal networks

Reference 44

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Learning Filter Basis for Convolutional Neural Network Compression Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 45

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This paper cites Cluster- ing convolutional kernels to compress deep neural networks.

Learning Filter Basis for Convolutional Neural Network Compression Cluster- ing convolutional kernels to compress deep neural networks

Reference 46

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This paper cites Factorized convolutional neural networks.

Learning Filter Basis for Convolutional Neural Network Compression Factorized convolutional neural networks

Reference 47

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This paper cites Learning structured sparsity in deep neural networks.

Learning Filter Basis for Convolutional Neural Network Compression Learning structured sparsity in deep neural networks

Reference 48

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Observation b0878a99-3aaf-47e8-a42e-84a4b1d0acd5 · outbound

This paper cites On sin- gle image scale-up using sparse-representations.

Learning Filter Basis for Convolutional Neural Network Compression On sin- gle image scale-up using sparse-representations

Reference 49

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Observation 1d385082-373f-4bf8-bc02-42fbe0b72d6f · outbound

This paper cites Beyond a gaussian denoiser: residual learning of deep CNN for image denoising.

Learning Filter Basis for Convolutional Neural Network Compression Beyond a gaussian denoiser: residual learning of deep CNN for image denoising

Reference 50

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This paper cites Learning deep cnn denoiser prior for image restoration.

Learning Filter Basis for Convolutional Neural Network Compression Learning deep cnn denoiser prior for image restoration

Reference 51

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Observation f301cff7-a0f4-4db6-bf2c-8a367a9f5a51 · outbound

This paper cites Deep plug- and-play super-resolution for arbitrary blur kernels.

Learning Filter Basis for Convolutional Neural Network Compression Deep plug- and-play super-resolution for arbitrary blur kernels

Reference 52

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This paper cites Accelerating very deep convolutional networks for classi- ﬁcation and detection.

Learning Filter Basis for Convolutional Neural Network Compression Accelerating very deep convolutional networks for classi- ﬁcation and detection

Reference 53

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Observation 70067cfe-c68e-4a03-8979-0c2eb9eaf7df · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Learning Filter Basis for Convolutional Neural Network Compression Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 54

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This paper cites Less is more: Towards compact cnns.

Learning Filter Basis for Convolutional Neural Network Compression Less is more: Towards compact cnns

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Learning Filter Basis for Convolutional Neural Network Compression Trained Ternary Quantization

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