Paper Citation Record · LEDGER

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.03930.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.03930 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:01:23.120783Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy24
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd759f33-eb18-4491-a97c-f9cd6b39c2e1 · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Tensorﬂow: A system for large-scale machine learning

Reference 1

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Observation f04e3bde-2da1-418a-896d-a9b414e8b735 · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 2

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This paper cites Imagenet: A large-scale hierarchical im- age database.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Imagenet: A large-scale hierarchical im- age database

Reference 3

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Observation ec38426b-cc8b-4444-8794-66e1289d7c9b · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Exploiting linear structure within con- volutional networks for efﬁcient evaluation

Reference 4

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Observation 495df0de-e0f9-41c6-8e36-c1a9dbce1755 · outbound

This paper cites Centripetal sgd for pruning very deep convolutional networks with complicated structure.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Centripetal sgd for pruning very deep convolutional networks with complicated structure

Reference 5

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Observation f082ea4c-a092-4259-9985-6497bb911512 · outbound

This paper cites Approximated oracle ﬁlter pruning for destructive cnn width optimization.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Approximated oracle ﬁlter pruning for destructive cnn width optimization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.363713Z

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Observation b4715e49-a1d0-475f-a05c-70ab6bd91707 · outbound

This paper cites Auto-balanced ﬁlter pruning for efﬁcient convolu- tional neural networks.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Auto-balanced ﬁlter pruning for efﬁcient convolu- tional neural networks

Reference 7

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Observation c51fb5b8-9ab3-4b96-adee-8078a35cbb44 · outbound

This paper cites Tensorﬂow-alexnet.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Tensorﬂow-alexnet

Reference 8

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Observation 6dd6d7d2-68f3-4caa-a3b9-e629e227d1a4 · outbound

This paper cites Dynamic net- work surgery for efﬁcient dnns.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Dynamic net- work surgery for efﬁcient dnns

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.206424Z

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Observation 7bf52b62-66ad-413b-9e78-8f4c4de720dc · outbound

This paper cites Deep learning with limited numerical precision.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Deep learning with limited numerical precision

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.174926Z

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Observation 36b0b197-0880-4638-b34d-6e9aea7059f6 · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 11

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Observation e61d30d1-c03e-4fe2-a1ba-ebb8f034fd91 · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Learning both weights and connections for efﬁcient neural network

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.135267Z

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Observation 65bda889-9683-4b89-a7d6-4fa7c747f81e · outbound

This paper cites Deep residual learning for image recognition.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Deep residual learning for image recognition

Reference 13

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Observation 7ef02f97-0145-4ee9-bcfa-da52311d65a5 · outbound

This paper cites Squeeze-and-excitation net- works.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Squeeze-and-excitation net- works

Reference 14

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Observation 3b454507-ae8b-45b7-ad47-2a28ebd5f5e2 · outbound

This paper cites Densely connected convolutional networks.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Densely connected convolutional networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.028246Z

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Observation 5a60ba0b-8d1d-4a56-a75d-c426cd398d0b · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.993072Z

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Observation 2c189d54-661c-4469-a989-f4428944f245 · outbound

This paper cites Speeding up Convolutional Neural Networks with Low Rank Expansions.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 17

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Observation 16ab1b47-a6d1-4288-8b74-cbcacc9592cd · outbound

This paper cites Flattened Convolutional Neural Networks for Feedforward Acceleration.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Flattened Convolutional Neural Networks for Feedforward Acceleration

Reference 18

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Observation 89f260df-8910-450d-9fd8-b9a234b5bd2a · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Learning multiple layers of features from tiny images

Reference 19

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Observation f198192a-40c8-4aed-ae3f-e5a2b11ebfff · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Imagenet classiﬁcation with deep convolutional neural net- works

Reference 20

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Observation df94be3c-8d44-445c-98f2-8b7499976a89 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Pruning Filters for Efficient ConvNets

Reference 21

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Observation 500d01f1-ed7f-4b04-8a08-9f6b57374458 · outbound

This paper cites Learning efﬁcient convolutional networks through network slimming.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Learning efﬁcient convolutional networks through network slimming

Reference 22

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Observation b1abd261-eb76-4d40-89dd-43a118466c31 · outbound

This paper cites Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

Reference 23

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This paper cites Thinet: A ﬁlter level pruning method for deep neural network compression.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Thinet: A ﬁlter level pruning method for deep neural network compression

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.850671Z

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Observation 1f012256-615b-4b3e-b364-95a3ba01f320 · outbound

This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 25

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Observation 325bcfb3-7a44-4a61-921e-cce742b9107c · outbound

This paper cites Automatic differentiation in pytorch.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Automatic differentiation in pytorch

Reference 26

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ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Xnor-net: Imagenet classiﬁcation using bi- nary convolutional neural networks

Reference 27

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Observation 4959bf40-f931-400f-908d-bcc6e79106c0 · outbound

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

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 28

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This paper cites Prac- tical bayesian optimization of machine learning algorithms.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Prac- tical bayesian optimization of machine learning algorithms

Reference 29

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This paper cites Design of kernels in convolutional neural networks for image classiﬁ- cation.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Design of kernels in convolutional neural networks for image classiﬁ- cation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.725930Z

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Observation db647bd8-1b42-4a14-9a69-3e9f18994aa8 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.679661Z

Source-reported events for the cited work

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Observation 5c3478f2-646c-4efd-b06b-b258b86a195a · outbound

This paper cites Going deeper with convolutions.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Going deeper with convolutions

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.642740Z

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Observation 0d08653f-8a83-4d87-83d0-078e9dcce039 · outbound

This paper cites Rethinking the inception archi- tecture for computer vision.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Rethinking the inception archi- tecture for computer vision

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:23.611112Z

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This paper cites Be- yond ﬁlters: Compact feature map for portable deep model.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Be- yond ﬁlters: Compact feature map for portable deep model

Reference 34

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This paper cites Wide Residual Networks.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Wide Residual Networks

Reference 35

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This paper cites Learning transferable architectures for scalable image recognition.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks Learning transferable architectures for scalable image recognition

Reference 36

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This paper cites IEEE Conference on , pages 248–255.

ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks IEEE Conference on , pages 248–255

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:01:24.492149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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