mlsanityData to 14 Oct 2026

MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks

arXiv 1711.06798 ↗First posted 18 Nov 2017cs.LG stat.MLcited by 94

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Abstract (arXiv)

We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2017.

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Citing papers per year: 2021: 31; 2022: 22; 2023: 15; 2024: 12; 2025: 11; 2026: 3.

94 papers.

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  1. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
  2. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 202
  3. 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 61
  4. 2021DECORE: Deep Compression with Reinforcement Learningcited by 29
  5. 2023Computationally-Efficient Neural Image Compression with Shallow Decoderscited by 23
  6. 2021ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximationscited by 21
  7. 2021Learned Gradient Compression for Distributed Deep Learningcited by 20
  8. 2021Layer-wise Model Pruning based on Mutual Informationcited by 19
  9. 2021Knowledge Evolution in Neural Networkscited by 18
  10. 2022Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyondcited by 17
  11. 2021Differentiable Network Pruning for Microcontrollerscited by 16
  12. 2023Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edgecited by 15
38 more
  1. 2022Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devicescited by 14
  2. 2021NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimizationcited by 13
  3. 2021LANA: Latency Aware Network Accelerationcited by 11
  4. 2022DyRep: Bootstrapping Training with Dynamic Re-parameterizationcited by 11
  5. 2021Dissecting FLOPs along input dimensions for GreenAI cost estimationscited by 10
  6. 2021Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniquescited by 9
  7. 2023PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimizationcited by 9
  8. 2024Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimallycited by 9
  9. 2025FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Modelscited by 9
  10. 2021Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNscited by 8
  11. 2022Dynamic ConvNets on Tiny Devices via Nested Sparsitycited by 8
  12. 2022Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoringcited by 8
  13. 2022Designing and Training of Lightweight Neural Networks on Edge Devices using Early Halting in Knowledge Distillationcited by 7
  14. 2023Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devicescited by 7
  15. 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihoodcited by 7
  16. 2022Update Compression for Deep Neural Networks on the Edgecited by 6
  17. 2022Robust and Energy-efficient PPG-based Heart-Rate Monitoringcited by 6
  18. 2022Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networkscited by 6
  19. 2024Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networkscited by 6
  20. 2021Input Invex Neural Networkcited by 4
  21. 2022Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networkscited by 4
  22. 2022Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Futurecited by 4
  23. 2021SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detectioncited by 3
  24. 2021Efficient Sparse Artificial Neural Networkscited by 3
  25. 2021Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicescited by 3
  26. 2021Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)cited by 3
  27. 2023Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVscited by 3
  28. 2025Optimal Brain Apoptosiscited by 3
  29. 2021A Deeper Look into Convolutions via Eigenvalue-based Pruningcited by 2
  30. 2021How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architecturescited by 2
  31. 2021Image Complexity Guided Network Compression for Biomedical Image Segmentationcited by 2
  32. 2022The Ecological Footprint of Neural Machine Translation Systemscited by 2
  33. 2022U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Searchcited by 2
  34. 2022Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networkscited by 2
  35. 2022Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristicscited by 2
  36. 2022MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensionscited by 2
  37. 2024Learnable Sparse Customization in Heterogeneous Edge Computingcited by 2
  38. 2025Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruningcited by 2

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