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Neural Network Compression

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Showing 151–193 of 193 papers

TitleStatusHype
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis—0
Deep Neural Network Compression for Aircraft Collision Avoidance Systems—0
Differentiable Joint Pruning and Quantization for Hardware Efficiency—0
Distilling Critical Paths in Convolutional Neural Networks—0
Distilling Pixel-Wise Feature Similarities for Semantic Segmentation—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
DP-Net: Dynamic Programming Guided Deep Neural Network Compression—0
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression—0
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching—0
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression—0
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection—0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Entropy-Constrained Training of Deep Neural Networks—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
Evaluation Metrics for DNNs Compression—0
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks—0
GranQ: Granular Zero-Shot Quantization with Channel-Wise Activation Scaling in QAT—0
Grokking as Compression: A Nonlinear Complexity Perspective—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM—0
HEMP: High-order Entropy Minimization for neural network comPression—0
How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?—0
Hybrid Tensor Decomposition in Neural Network Compression—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration—0
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition—0
Linearity-based neural network compression—0
Compressing 3DCNNs Based on Tensor Train Decomposition—0
Low-Rank Matrix Approximation for Neural Network Compression—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming—0
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning—0
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression—0
Model Compression Methods for YOLOv5: A Review—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression—0
MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compression—0
Multi-head Knowledge Distillation for Model Compression—0
Filter Distillation for Network Compression—0
NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES—0
Neural gradients are near-lognormal: improved quantized and sparse training—0
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction—0
Neural Network Compression for Noisy Storage Devices—0
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