% Key papers for CNN filter pruning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_filter_14214,
  title = {{Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2016_learning_55825,
  title = {{Learning Structured Sparsity in Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7205},
}

@inproceedings{neurips2017_the_56721,
  title = {{The Reversible Residual Network: Backpropagation Without Storing Activations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9009},
}

@inproceedings{cvpr2017_deep_12175,
  title = {{Deep Roots: Improving CNN Efficiency With Hierarchical Filter Groups}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2016_learning_11667,
  title = {{Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2021_convolutional_16756,
  title = {{Convolutional Neural Network Pruning With Structural Redundancy Reduction}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2019_centripetal_13930,
  title = {{Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2018_rethinking_28505,
  title = {{Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/315},
}

@inproceedings{iclr2017_faster_28361,
  title = {{Faster CNNs with Direct Sparse Convolutions and Guided Pruning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=rJPcZ3txx},
}

@inproceedings{aaai2018_auto_104003,
  title = {{Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12262},
}

@inproceedings{cvpr2019_compressing_13955,
  title = {{Compressing Convolutional Neural Networks via Factorized Convolutional Filters}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_gate_58314,
  title = {{Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13414},
}

@inproceedings{neurips2018_frequency_57145,
  title = {{Frequency-Domain Dynamic Pruning for Convolutional Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11124},
}

@inproceedings{neurips2020_scop_60758,
  title = {{SCOP: Scientific Control for Reliable Neural Network Pruning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16879},
}

@inproceedings{aaai2018_deeprebirth_105767,
  title = {{DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11876},
}

@inproceedings{icml2018_deep_36627,
  title = {{Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2219},
}

@inproceedings{cvpr2020_discrete_15412,
  title = {{Discrete Model Compression With Resource Constraint for Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2019_repr_14707,
  title = {{RePr: Improved Training of Convolutional Filters}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2018_efficient_28521,
  title = {{Efficient Sparse-Winograd Convolutional Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/297},
}

@inproceedings{icml2019_approximated_37161,
  title = {{Approximated Oracle Filter Pruning for Destructive CNN Width Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3835},
}

@inproceedings{aaai2024_reprune_7052,
  title = {{REPrune: Channel Pruning via Kernel Representative Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29370},
}

@inproceedings{cvpr2025_frequency_26323,
  title = {{Frequency-Biased Synergistic Design for Image Compression and Compensation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33354},
}

@inproceedings{aaai2024_soundcount_7371,
  title = {{SoundCount: Sound Counting from Raw Audio with Dyadic Decomposition Neural Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29134},
}