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A Novel Deep Learning Model Compression Algorithm

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf02478259openalex-snapshot
A logical calculus of the ideas immanent in nervous activity
inferredcites →https://doi.org/10.1016/j.neucom.2020.07.048openalex-snapshot
Adaptive multi-teacher multi-level knowledge distillation
inferredcites →https://doi.org/10.1017/cbo9780511754661openalex-snapshot
Quantization Noise
inferredcites →https://doi.org/10.1109/34.58871openalex-snapshot
Neural network ensembles
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
inferredcites →https://doi.org/10.1109/cvpr.2019.00447openalex-snapshot
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
inferredcites →https://doi.org/10.1109/cvpr.2019.00881openalex-snapshot
HAQ: Hardware-Aware Automated Quantization With Mixed Precision
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00159openalex-snapshot
Towards Efficient Model Compression via Learned Global Ranking
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00160openalex-snapshot
HRank: Filter Pruning Using High-Rank Feature Map
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00204openalex-snapshot
Towards Unified INT8 Training for Convolutional Neural Network
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01170openalex-snapshot
Swin Transformer V2: Scaling Up Capacity and Resolution
inferredcites →https://doi.org/10.1109/iccv.2019.00381openalex-snapshot
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
inferredcites →https://doi.org/10.1109/msp.2017.2765695openalex-snapshot
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1145/3005348openalex-snapshot
Structured Pruning of Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.24963/ijcai.2018/309openalex-snapshot
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.3390/electronics10010072openalex-snapshot
Layer-Wise Network Compression Using Gaussian Mixture Model
inferredcites →https://doi.org/10.3390/electronics10040396openalex-snapshot
Optimising Hardware Accelerated Neural Networks with Quantisation and a Knowledge Distillation Evolutionary Algorithm
inferredcites →https://doi.org/10.3390/electronics10080886openalex-snapshot
Improving Model Capacity of Quantized Networks with Conditional Computation
inferredcites →https://doi.org/10.3390/electronics11060858openalex-snapshot
MobileNets Can Be Lossily Compressed: Neural Network Compression for Embedded Accelerators
inferredcites →https://doi.org/10.3390/electronics9101666openalex-snapshot
Accelerating Event Detection with DGCNN and FPGAs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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PCA-based compression of deep LSTMs: a robust framework for uncertain industrial systems
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Bibliometric analysis of deep learning in plant disease management
inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104668openalex-snapshot
An optimized deep supervised hashing model for fast image retrieval
inferred← citeshttps://doi.org/10.1109/access.2025.3590316openalex-snapshot
Empirical Evaluation on GPU, Overclocking, and LoRA for Deep Learning on Embedded Systems
inferred← citeshttps://doi.org/10.1109/ised67359.2025.11405082openalex-snapshot
A Multi Stage Framework for Efficient Deep Neural Network Compression on Edge Devices
inferred← citeshttps://doi.org/10.1109/taes.2022.3216579openalex-snapshot
Autonomous Driving for Natural Paths Using an Improved Deep Reinforcement Learning Algorithm
inferred← citeshttps://doi.org/10.1109/tccn.2026.3705804openalex-snapshot
Energy-Efficient Radio Modulation Classification via CNN-to-CSNN Conversion
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3486109openalex-snapshot
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
inferred← citeshttps://doi.org/10.22399/ijcesen.944openalex-snapshot
Adaptive Knowledge-Guided Pruning Algorithm AKGP with Dynamic Weight Allocation for Model Compression
inferred← citeshttps://doi.org/10.3390/electronics11081212openalex-snapshot
A Novel Fusion Pruning Algorithm Based on Information Entropy Stratification and IoT Application
inferred← citeshttps://doi.org/10.3390/electronics12051208openalex-snapshot
Deep Learning Architecture Improvement Based on Dynamic Pruning and Layer Fusion
inferred← citeshttps://doi.org/10.3390/electronics12071694openalex-snapshot
Pruning- and Quantization-Based Compression Algorithm for Number of Mixed Signals Identification Network
inferred← citeshttps://doi.org/10.3390/electronics13030557openalex-snapshot
Guest Editorial: Foreword to the Special Issue on Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition
inferred← citeshttps://doi.org/10.3390/s24041149openalex-snapshot
A Comparative Study of Preprocessing and Model Compression Techniques in Deep Learning for Forest Sound Classification
inferred← citeshttps://doi.org/10.3390/s25020531openalex-snapshot
A Comprehensive Survey of Deep Learning Approaches in Image Processing
inferred← citeshttps://doi.org/10.47611/jsrhs.v13i1.6102openalex-snapshot
The Application of Convolutional Neural Networks in Organ-on-a-Chip Technology: A Review
inferred← citeshttps://doi.org/10.52339/tjet.v42i2.853openalex-snapshot
Deep Learning Model Compression Techniques: Advances, Opportunities, and Perspective
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