Model Compression using Progressive Channel Pruning | Paper Citation Record

Observation f10575e8-5587-4a49-ab50-94d040ac9cb1 · outbound

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

Model Compression using Progressive Channel Pruning Channel pruning for accelerating very deep neural networks,

Reference 1

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Observation bd43d0d7-2107-4927-bacc-7830ecbfc558 · outbound

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

Model Compression using Progressive Channel Pruning Thinet: A filter level pruning method for deep neural network compression,

Reference 2

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Observation 55ea11c0-fb00-4d54-98a6-90106c466bc6 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Model Compression using Progressive Channel Pruning Imagenet large scale visual recognition challenge,

Reference 3

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Observation 2da4a1ac-0c05-4989-8fbc-fabfca5934ce · outbound

This paper cites Adapting visual category models to new domains,.

Model Compression using Progressive Channel Pruning Adapting visual category models to new domains,

Reference 4

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Observation 8f7cbac6-0a39-44c5-9368-6b207e109d22 · outbound

This paper cites Domain-adversarial training of neural networks,.

Model Compression using Progressive Channel Pruning Domain-adversarial training of neural networks,

Reference 5

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Observation 6785b1a5-ab8d-4cdf-9f27-6413c5b36e75 · outbound

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

Model Compression using Progressive Channel Pruning Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 6

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Observation bbb067f9-1cec-41be-aca3-b14956a58fb7 · outbound

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

Model Compression using Progressive Channel Pruning Speeding up convolutional neural networks with low rank expansions,

Reference 7

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Observation 60b64172-3430-4cff-9dc3-8a767f5c4402 · outbound

This paper cites Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications.

Model Compression using Progressive Channel Pruning Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Reference 8

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Observation 4a82c868-c1b8-4d8f-a027-100914c3d2f7 · outbound

This paper cites Compressing Deep Convolutional Networks using Vector Quantization.

Model Compression using Progressive Channel Pruning Compressing Deep Convolutional Networks using Vector Quantization

Reference 9

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Observation 3b4c7ec9-7a08-469c-9008-267eb666308b · outbound

This paper cites Restructuring of deep neural network acoustic models with singular value decomposition.

Model Compression using Progressive Channel Pruning Restructuring of deep neural network acoustic models with singular value decomposition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:10.002210Z

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Observation e1fb3860-04e4-46ad-a64b-08849c89b7de · outbound

This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks,.

Model Compression using Progressive Channel Pruning Xnor-net: Imagenet classification using binary convolutional neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:09.831603Z

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Observation 50d4c7d4-d72b-44ae-af3c-7f49d2aa360c · outbound

This paper cites Compressing large language models by joint sparsification and quantization,.

Model Compression using Progressive Channel Pruning Compressing large language models by joint sparsification and quantization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:09.568326Z

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Observation 084e49bc-8939-4dc9-b109-b8dea4a5e72e · outbound

This paper cites Ptq4sam: Post-training quantization for segment anything,.

Model Compression using Progressive Channel Pruning Ptq4sam: Post-training quantization for segment anything,

Reference 13

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Observation f8431757-c371-4dc9-bd00-79443307abff · outbound

This paper cites Llmcbench: Benchmarking large language model compression for efficient deployment,.

Model Compression using Progressive Channel Pruning Llmcbench: Benchmarking large language model compression for efficient deployment,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:09.397779Z

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Observation 45c9514d-514d-41ba-8d16-5a304cd3c0a8 · outbound

This paper cites Lcnn: Lookup-based convolutional neural network,.

Model Compression using Progressive Channel Pruning Lcnn: Lookup-based convolutional neural network,

Reference 15

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Observation 2a706fd6-ecfa-4dd8-b74f-313e937dec63 · outbound

This paper cites Fast algorithms for convolutional neural net- works,.

Model Compression using Progressive Channel Pruning Fast algorithms for convolutional neural net- works,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:09.001090Z

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Observation 66593c66-7d24-4162-bcb4-69ecd03f32d2 · outbound

This paper cites Fast training of convolutional networks through ffts,.

Model Compression using Progressive Channel Pruning Fast training of convolutional networks through ffts,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:08.828285Z

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Observation 7f6663d5-e15a-415a-8950-39ea4008b579 · outbound

This paper cites Fast Convolutional Nets With fbfft: A GPU Performance Evaluation.

Model Compression using Progressive Channel Pruning Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Reference 18

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Observation 4b0b2110-44c2-410e-b8eb-d3ab3ae7cc44 · outbound

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

Model Compression using Progressive Channel Pruning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 19

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Observation 9dc43b45-a189-448b-bb5d-6a7f67bee19e · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices.

Model Compression using Progressive Channel Pruning Shufflenet: An extremely efficient convolutional neural network for mobile devices

Reference 20

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Observation 7b3fd17b-d3d0-4285-8ae3-80182a7d4929 · outbound

This paper cites Lta-pcs: Learnable task-agnostic point cloud sampling,.

Model Compression using Progressive Channel Pruning Lta-pcs: Learnable task-agnostic point cloud sampling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:08.440664Z

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Observation def1df05-85d1-46f2-b1bf-508cf670e661 · outbound

This paper cites EIE: Efficient inference engine on compressed deep neural network,.

Model Compression using Progressive Channel Pruning EIE: Efficient inference engine on compressed deep neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:08.254352Z

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Observation d7b82c64-dee2-4e75-94ba-3a527df444de · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

Model Compression using Progressive Channel Pruning Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 23

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Observation ff08e880-7ac0-4e71-9d4c-06ff86c52f0a · outbound

This paper cites Pruning filters for efficient convnets,.

Model Compression using Progressive Channel Pruning Pruning filters for efficient convnets,

Reference 24

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Observation 6d276ee8-0cd3-4617-bab4-df05f9cfc645 · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

Model Compression using Progressive Channel Pruning Pruning convolutional neural networks for resource efficient inference,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:07.874892Z

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Observation 37a71b0a-8a06-445f-8df9-a337fa69114a · outbound

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

Model Compression using Progressive Channel Pruning Learning both weights and connections for efficient neural network,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:07.700963Z

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Observation 8926733b-41e0-4067-ad81-9ace7340c979 · outbound

This paper cites Multi-dimensional pruning: A unified framework for model compression,.

Model Compression using Progressive Channel Pruning Multi-dimensional pruning: A unified framework for model compression,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:07.526591Z

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Observation 4a72cf81-7b7c-48e6-bdf9-fe275f7c1e0a · outbound

This paper cites Multidimensional pruning and its exten- sion: A unified framework for model compression,.

Model Compression using Progressive Channel Pruning Multidimensional pruning and its exten- sion: A unified framework for model compression,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:07.334365Z

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Observation dd4003b4-c3cb-4048-bacb-3699a6c540a3 · outbound

This paper cites Channel pruning guided by classifica- tion loss and feature importance,.

Model Compression using Progressive Channel Pruning Channel pruning guided by classifica- tion loss and feature importance,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:07.142595Z

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Observation 244fd708-f320-412e-91cf-ed2fd9be3dc5 · outbound

This paper cites Ptsbench: A comprehensive post-training sparsity benchmark towards algorithms and models,.

Model Compression using Progressive Channel Pruning Ptsbench: A comprehensive post-training sparsity benchmark towards algorithms and models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.953185Z

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Observation dc8a856b-a8a9-4547-9f05-375acf66c7f3 · outbound

This paper cites Jointpruning: Pruning networks along multi- ple dimensions for efficient point cloud processing,.

Model Compression using Progressive Channel Pruning Jointpruning: Pruning networks along multi- ple dimensions for efficient point cloud processing,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.812189Z

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Observation b0fc8560-c8d9-4c99-92a7-d5ff708d6fc1 · outbound

This paper cites Cbanet: Towards complexity and bitrate adaptive deep image compression using a single network,.

Model Compression using Progressive Channel Pruning Cbanet: Towards complexity and bitrate adaptive deep image compression using a single network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.677790Z

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Observation d122ff44-de00-4638-8ec5-26486c3a5ede · outbound

This paper cites 3d-pruning: A model compression framework for efficient 3d action recognition,.

Model Compression using Progressive Channel Pruning 3d-pruning: A model compression framework for efficient 3d action recognition,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.500638Z

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Observation f9ee53e9-dac4-468e-8bc9-8b9b3e5c11bf · outbound

This paper cites Exploiting linear structure within convolutional networks for efficient evaluation,.

Model Compression using Progressive Channel Pruning Exploiting linear structure within convolutional networks for efficient evaluation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.349884Z

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Observation 113698ac-eb99-42e3-bbd7-da6daee97e8d · outbound

This paper cites Fast R-CNN,.

Model Compression using Progressive Channel Pruning Fast R-CNN,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.205944Z

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Observation 0a81cb36-59f5-4a05-b774-2ac9994e47a4 · outbound

This paper cites Variational convolutional neural network pruning,.

Model Compression using Progressive Channel Pruning Variational convolutional neural network pruning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:06.066079Z

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Observation 1b6584e6-0d8e-4bd3-821d-0988f974b214 · outbound

This paper cites Towards optimal structured cnn pruning via generative adversarial learning,.

Model Compression using Progressive Channel Pruning Towards optimal structured cnn pruning via generative adversarial learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:05.848285Z

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Observation 727f9a36-b4f3-41f6-8718-62455f79bd97 · outbound

This paper cites Collaborative channel pruning for deep networks,.

Model Compression using Progressive Channel Pruning Collaborative channel pruning for deep networks,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:05.463085Z

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Observation 98ee59cb-e243-40bc-a40e-122599f01f31 · outbound

This paper cites AMC: Automl for model compression and acceleration on mobile devices,.

Model Compression using Progressive Channel Pruning AMC: Automl for model compression and acceleration on mobile devices,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:05.165002Z

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Observation 512674de-7552-47bf-9428-04dded031f7e · outbound

This paper cites Cooperative pruning in cross-domain deep neural network compression,.

Model Compression using Progressive Channel Pruning Cooperative pruning in cross-domain deep neural network compression,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:04.883435Z

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Observation 751af9da-98a6-4e1e-b553-65621f159080 · outbound

This paper cites Learning transferable features with deep adaptation networks,.

Model Compression using Progressive Channel Pruning Learning transferable features with deep adaptation networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:04.675494Z

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Observation bd11546f-5921-42a5-bf2b-9c4ebc88ad50 · outbound

This paper cites Unsupervised domain adaptation with residual transfer networks,.

Model Compression using Progressive Channel Pruning Unsupervised domain adaptation with residual transfer networks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:04.447776Z

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Observation 22b595dd-4867-4924-a87f-7138d5032747 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation,.

Model Compression using Progressive Channel Pruning Deep coral: Correlation alignment for deep domain adaptation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:04.206117Z

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Observation 28bb193a-425b-416b-99b6-cbb75f63c44d · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

Model Compression using Progressive Channel Pruning Deep Domain Confusion: Maximizing for Domain Invariance

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:00.207054Z

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Observation 349555f5-1c69-4902-aae5-a5ea07e7ec08 · outbound

This paper cites Unsupervised pixel-level domain adaptation with generative adversarial networks,.

Model Compression using Progressive Channel Pruning Unsupervised pixel-level domain adaptation with generative adversarial networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:03.948821Z

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Observation 419bd0d5-6e76-4011-aa9e-241fb61c5c54 · outbound

This paper cites Domain separation networks,.

Model Compression using Progressive Channel Pruning Domain separation networks,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:03.742309Z

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Observation 61668ab7-9f32-4eb3-a21a-61df4a9ccf9f · outbound

This paper cites Unsupervised domain adaptation by backpropagation,.

Model Compression using Progressive Channel Pruning Unsupervised domain adaptation by backpropagation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:03.444178Z

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Observation e7bddf99-31fb-4488-8f22-1cb9585fbe5f · outbound

This paper cites Coupled generative adversarial networks,.

Model Compression using Progressive Channel Pruning Coupled generative adversarial networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:03.200867Z

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Observation aef4c59c-ce10-4ba9-80fb-44bc41ae5e98 · outbound

This paper cites Adversarial discrim- inative domain adaptation,.

Model Compression using Progressive Channel Pruning Adversarial discrim- inative domain adaptation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:02.927221Z

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Observation 677e2c20-e3ba-4582-a40e-6a9cc9d33155 · outbound

This paper cites Collaborative and adversarial network for unsupervised domain adaptation,.

Model Compression using Progressive Channel Pruning Collaborative and adversarial network for unsupervised domain adaptation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:02.569616Z

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Observation 12cc13bf-3339-426c-860a-760a0df3dc07 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Model Compression using Progressive Channel Pruning Very deep convolutional networks for large-scale image recognition,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:01.290946Z

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Observation cb211417-1a6d-4bad-bfa5-3ec5ad529c1a · outbound

This paper cites Deep residual learning for image recognition,.

Model Compression using Progressive Channel Pruning Deep residual learning for image recognition,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:01.469607Z

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Observation fd002ad2-229e-4950-bd11-f3a0dd594f8e · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Model Compression using Progressive Channel Pruning Imagenet classification with deep convolutional neural networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:02.283739Z

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Observation a30152bb-d331-49bf-978b-190feac081ba · outbound

This paper cites NISP: Pruning networks using neuron importance score propagation,.

Model Compression using Progressive Channel Pruning NISP: Pruning networks using neuron importance score propagation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:02.033096Z

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