Lyken17 / SparseNet

[ECCV 2018] Sparsely Aggreagated Convolutional Networks https://arxiv.org/abs/1801.05895

SparseNet

Sparsely Aggregated Convolutional Networks [PDF]

Ligeng Zhu, Ruizhi Deng, Michael Maire, Zhiwei Deng, Greg Mori, Ping Tan

What is SparseNet?

SparseNet is a network architecture that only aggregates previous layers with exponential offset, for example, i - 1, i - 2, i - 4, i - 8, i - 16 ...

Why use SparseNet?

The connectivity pattern yields state-of-the-art arruacies on small dataset CIFAR/10/100. On large scale ILSVRC 2012 (ImageNet) dataset, SparseNet achieves similar accuracy as ResNet and DenseNet, while only using much less parameters.

Better Performance

Without BC	With BC
Architecture Params CIFAR 100 DenseNet-40-12 1.1M 24.79 DenseNet-100-12 7.2M 20.97 DenseNet-100-24 28.28M 19.61 --- --- --- SparseNet-40-24 0.76M 24.65 SparseNet-100-36 5.65M 20.50 SparseNet-100-{16,32,64} 7.22M 19.49	Architecture Params CIFAR 100 DenseNet-100-12 0.8M 22.62 DenseNet-250-24 15.3M 17,6 DenseNet-190-40 25.6M 17.53 --- --- --- SparseNet-100-24 1.46M 22.12 SparseNet-100-{16,32,64} 4.38M 19.71 SparseNet-100-{32,64,128} 16.72M 17.71

Efficient Parameter Utilization

• Parameter efficiency on CIFAR

• Paramter efficiency on ImageNet

  We notice sparsenet shows comparable efficiency even compared with pruned models.

Pretrained model

Refer for source folder.

Cite

If SparseNet helps your research, please cite our work :)

@article{DBLP:journals/corr/abs-1801-05895,
  author    = {Ligeng Zhu and
               Ruizhi Deng and
               Michael Maire and
               Zhiwei Deng and
               Greg Mori and
               Ping Tan},
  title     = {Sparsely Aggregated Convolutional Networks},
  journal   = {CoRR},
  volume    = {abs/1801.05895},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.05895},
  archivePrefix = {arXiv},
  eprint    = {1801.05895},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1801-05895},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}