awesome-pruning
github.com/he-y/awesome-pruning ↗A curated list of neural network pruning resources.
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276
Curated Resources
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8 hours ago
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202320222021202020192018201720162015
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me 2021 resources from awesome-pruning"
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2021
- Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
ICML
- AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
NeurIPS
- Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
ICCV
- A Gradient Flow Framework For Analyzing Network Pruning
ICLR
- A Probabilistic Approach to Neural Network Pruning
ICML
- Auto Graph Encoder-Decoder for Neural Network Pruning
ICCV
2019
- Accelerate CNN via Recursive Bayesian Pruning
ICCV
- Adversarial Robustness vs Model Compression, or Both?
ICCV
- Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github
ICML
- AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
NeurIPS
- Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
CVPR
- Co-Evolutionary Compression for Unpaired Image Translation
ICCV
2020
- Accelerating CNN Training by Pruning Activation Gradients
ECCV
- Adversarial Neural Pruning with Latent Vulnerability Suppression
ICML
- APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
CVPR
- A Signal Propagation Perspective for Pruning Neural Networks at Initialization
ICLR (Spotlight)
- AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
AAAI
- Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-Based Approach
CVPR
2018
- Accelerating Convolutional Networks via Global & Dynamic Filter Pruning
IJCAI
- Amc: Automl for model compression and acceleration on mobile devices
ECCV
- A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
ECCV
- CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
CVPR
- Compressing Neural Networks using the Variational Information Bottleneck
ICML
- Constraint-Aware Deep Neural Network Compression
ECCV
2022
- Advancing Model Pruning via Bi-level Optimization
NeurIPS
- Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
NeurIPS
- An Operator Theoretic View On Pruning Deep Neural Networks
ICLR
- Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation
NeurIPS
- Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning
ECCV
- CHEX: CHannel EXploration for CNN Model Compression
CVPR
2023
- A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis
ICLR
- A Unified Framework for Soft Threshold Pruning
ICLR
- Bit-Pruning: A Sparse Multiplication-Less Dot-Product
ICLR
- CrAM: A Compression-Aware Minimizer
ICLR
- DepthFL : Depthwise Federated Learning for Heterogeneous Clients
ICLR
- DFPC: Data flow driven pruning of coupled channels without data
ICLR
2017
- Bayesian Compression for Deep Learning
NeurIPS
- Channel pruning for accelerating very deep neural networks
ICCV
- Combined Group and Exclusive Sparsity for Deep Neural Networks
ICML
- Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
CVPR
- Learning Efficient Convolutional Networks Through Network Slimming
ICCV
- Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
NeurIPS
Showing a sample of 276 resources. View the full list on GitHub →