mlsanityData to 7 Oct 2026

CondenseNet: An Efficient DenseNet using Learned Group Convolutions

arXiv 1711.09224 ↗First posted 25 Nov 2017cs.CVcited by 121

Not on the map. Why?

Abstract (arXiv)

Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as MobileNets and ShuffleNets.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 1; 2020: 0; 2021: 40; 2022: 25; 2023: 25; 2024: 18; 2025: 8; 2026: 4.

121 papers.

Sort:

By area:

  1. 2021Dynamic Neural Networks: A Surveycited by 405
  2. 2024The Unreasonable Ineffectiveness of the Deeper Layerscited by 219
  3. 2023Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networkscited by 129
  4. 2021State Entropy Maximization with Random Encoders for Efficient Explorationcited by 107
  5. 2021Weakly Supervised Object Localization and Detection: A Surveycited by 84
  6. 2022The Effects of Regularization and Data Augmentation are Class Dependentcited by 67
  7. 2022Dynamic Multimodal Fusioncited by 54
  8. 2021DeepReDuce: ReLU Reduction for Fast Private Inferencecited by 50
  9. 2022Online Continual Learning for Embedded Devicescited by 48
  10. 2022Glance and Focus Networks for Dynamic Visual Recognitioncited by 41
  11. 2021HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformerscited by 39
  12. 2024Lightweight Deep Learning for Resource-Constrained Environments: A Surveycited by 38
38 more
  1. 2021Towards Compact CNNs via Collaborative Compressioncited by 36
  2. 2021CondenseNet V2: Sparse Feature Reactivation for Deep Networkscited by 34
  3. 2021Dynamic Resolution Networkcited by 31
  4. 2021Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learningcited by 30
  5. 2023Masked Contrastive Graph Representation Learning for Age Estimationcited by 27
  6. 2023Latency-aware Unified Dynamic Networks for Efficient Image Recognitioncited by 26
  7. 2022Latency-aware Spatial-wise Dynamic Networkscited by 25
  8. 2023DAT++: Spatially Dynamic Vision Transformer with Deformable Attentioncited by 21
  9. 2021Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolutioncited by 19
  10. 2021Learning Prototype-oriented Set Representations for Meta-Learningcited by 19
  11. 2021Knowledge Evolution in Neural Networkscited by 18
  12. 2022Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolutioncited by 18
  13. 2023Computation-efficient Deep Learning for Computer Vision: A Surveycited by 14
  14. 2022RepGhost: A Hardware-Efficient Ghost Module via Re-parameterizationcited by 13
  15. 2023Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecyclecited by 13
  16. 2025Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perceptioncited by 13
  17. 2021Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddingscited by 12
  18. 2022SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruningcited by 12
  19. 2022An Examination of Bias of Facial Analysis based BMI Prediction Modelscited by 10
  20. 2024Strengthening Layer Interaction via Dynamic Layer Attentioncited by 10
  21. 2021Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniquescited by 9
  22. 2022PAD-Net: An Efficient Framework for Dynamic Networkscited by 9
  23. 2021Neural Architecture Search as Program Transformation Explorationcited by 8
  24. 2021RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Searchcited by 8
  25. 2025UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approachcited by 8
  26. 2023DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classificationcited by 7
  27. 2023Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group Convolutioncited by 7
  28. 2021Set-to-Sequence Methods in Machine Learning: a Reviewcited by 6
  29. 2021Compacting Deep Neural Networks for Internet of Things: Methods and Applicationscited by 6
  30. 2021Dynamic Domain Adaptation for Efficient Inferencecited by 6
  31. 2023Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Networkcited by 6
  32. 2024Multi-Scale Fusion for Object Representationcited by 6
  33. 2022Selective Residual M-Net for Real Image Denoisingcited by 5
  34. 2023Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Modelscited by 5
  35. 2023Adam-family Methods with Decoupled Weight Decay in Deep Learningcited by 5
  36. 2024Grouped Discrete Representation for Object-Centric Learningcited by 5
  37. 2025UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancementcited by 5
  38. 2021Two-level Group Convolutioncited by 4

50 most cited of 121 All 121 (JSON)