mlsanityData to 7 Oct 2026

Learning to Prune Filters in Convolutional Neural Networks

arXiv 1801.07365 ↗First posted 23 Jan 2018cs.CVcited by 25

Not on the map. Why?

Abstract (arXiv)

Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumptions. This paper presents a learning algorithm to simplify and speed up these CNNs. Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way. With the help of a novel reward function, our agents removes a significant number of filters in CNNs while maintaining performance at a desired level. Moreover, this method provides an easy control of the tradeoff between network performance and its scale. Per- formance of our algorithm is validated with comprehensive pruning experiments on several popular CNNs for visual recognition and semantic segmentation tasks.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 3; 2023: 9; 2024: 2; 2025: 1; 2026: 1.

25 papers.

Sort:

  1. 2021Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNNcited by 9
  2. 2021Equal Bits: Enforcing Equally Distributed Binary Network Weightscited by 9
  3. 2024Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignmentcited by 9
  4. 2022A Rubric for Human-like Agents and NeuroAIcited by 8
  5. 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challengescited by 7
  6. 2023Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Trainingcited by 5
  7. 2024Holistic Adversarially Robust Pruningcited by 5
  8. 2021Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillationcited by 4
  9. 2023Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizercited by 4
  10. 2023UPSCALE: Unconstrained Channel Pruningcited by 4
  11. 2021Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reductioncited by 3
  12. 2021L2PF -- Learning to Prune Fastercited by 2
13 more
  1. 2021Evolving Transferable Neural Pruning Functionscited by 1
  2. 2021Exploring Gradient Flow Based Saliency for DNN Model Compressioncited by 1
  3. 2023Learning Activation Functions for Sparse Neural Networkscited by 1
  4. 2023Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgettingcited by 1
  5. 2021Convolutional Neural Network Simplification with Progressive Retrainingcited by 0
  6. 2021Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementationcited by 0
  7. 2022Real-Time Online Skeleton Extraction and Gesture Recognition on Peppercited by 0
  8. 2023Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruningcited by 0
  9. 2023CP-CNN: Core-Periphery Principle Guided Convolutional Neural Networkcited by 0
  10. 2023A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systemscited by 0
  11. 2023Pruning Convolutional Filters via Reinforcement Learning with Entropy Minimizationcited by 0
  12. 2025Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learningcited by 0
  13. 2026Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimationcited by 0