Methods › Computer Vision › Convolutions › Depthwise Convolution › Papers, page 14

Depthwise Convolution

Papers archive 2025-07-28

archive papers tagged: 1,321 · with a code link: 549 · where Syntology ran a sample: 141 (126 with a run with no instrument failure, 15 where every run was a failure of Syntology's instrument) Syntology

Show: all tagged papersonly where code ran (141 of 1,321 tagged: 126 with a run with no instrument failure, 15 where every run was a failure of Syntology's instrument)

Page 14 of 14: papers 1,301 to 1,321 of 1,321, newest first by the archive's date (ties by slug), in archive order.

Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.

• NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
  9 Apr 2018 · 4 repositories · arXiv:1804.03230
  Syntology 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
• Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
  27 Mar 2018 · 3 repositories · arXiv:1803.09926
• Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
  24 Mar 2018 · 2 repositories · arXiv:1803.09127
• A Quantization-Friendly Separable Convolution for MobileNets
  22 Mar 2018 · 1 repository · arXiv:1803.08607
• HENet:A Highly Efficient Convolutional Neural Networks Optimized for Accuracy, Speed and Storage
  7 Mar 2018 · 1 repository · arXiv:1803.02742
• RTSeg: Real-time Semantic Segmentation Comparative Study
  7 Mar 2018 · 2 repositories · arXiv:1803.02758
• FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy
  11 Feb 2018 · 3 repositories · arXiv:1802.03750
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
  7 Feb 2018 · 78 repositories · arXiv:1802.02611
  Syntology community repositories only · 44 ran (of which 17 constructed an object rather than computing a result; 28 with no instrument failure: 2 honoured, 0 violated, 26 with no contract checked; 16 where Syntology's instrument failed) · 28 unverified (of 72 harvested samples) · 40 pointer-only (licence)
• EffNet: An Efficient Structure for Convolutional Neural Networks
  19 Jan 2018 · 3 repositories · arXiv:1801.06434
• MobileNetV2: Inverted Residuals and Linear Bottlenecks
  13 Jan 2018 · 159 repositories · arXiv:1801.04381
  Syntology 85 ran (of which 40 constructed an object rather than computing a result; 65 with no instrument failure: 8 honoured, 0 violated, 57 with no contract checked; 20 where Syntology's instrument failed) · 26 unverified (of 111 harvested samples) · 64 pointer-only (licence)
• Learning Graph Convolution Filters from Data Manifold
  1 Jan 2018 · 0 repositories
• Progressive Neural Architecture Search
  2 Dec 2017 · 18 repositories · arXiv:1712.00559
  Syntology official (archive's flag): 1 ran · 3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples) · 2 pointer-only (licence)
• Receptive Field Block Net for Accurate and Fast Object Detection
  21 Nov 2017 · 7 repositories · arXiv:1711.07767
  Syntology official: no sample here; runs from other or unrecorded repositories · 3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples) · 3 pointer-only (licence)
• Learning Depthwise Separable Graph Convolution from Data Manifold
  31 Oct 2017 · 0 repositories · arXiv:1710.11577
• Channel Pruning for Accelerating Very Deep Neural Networks
  19 Jul 2017 · 1 repository · arXiv:1707.06168
• Interleaved Group Convolutions for Deep Neural Networks
  10 Jul 2017 · 2 repositories · arXiv:1707.02725
• ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
  4 Jul 2017 · 38 repositories · arXiv:1707.01083
• Xception: Deep Learning With Depthwise Separable Convolutions
  1 Jul 2017 · 7 repositories
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
  17 Apr 2017 · 159 repositories · arXiv:1704.04861
  Syntology official: no sample here; runs from other or unrecorded repositories · 53 ran (of which 28 constructed an object rather than computing a result; 44 with no instrument failure: 4 honoured, 0 violated, 40 with no contract checked; 9 where Syntology's instrument failed) · 30 unverified (of 83 harvested samples) · 48 pointer-only (licence)
• Towards a New Interpretation of Separable Convolutions
  16 Jan 2017 · 0 repositories · arXiv:1701.04489
• Xception: Deep Learning with Depthwise Separable Convolutions
  7 Oct 2016 · 41 repositories · arXiv:1610.02357
  Syntology 7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified (of 15 harvested samples)