Papers with Code 아카이브
Neural Network Compression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 4
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compression
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Certified Neural Approximations of Nonlinear Dynamics
Low-Rank Matrix Approximation for Neural Network Compression
GranQ: Granular Zero-Shot Quantization with Channel-Wise Activation Scaling in QAT
Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding
A Novel Structure-Agnostic Multi-Objective Approach for Weight-Sharing Compression in Deep Neural Networks
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
Unified Framework for Neural Network Compression via Decomposition and Optimal Rank Selection
TropNNC: Structured Neural Network Compression Using Tropical Geometry
Convolutional Neural Network Compression Based on Low-Rank Decomposition
Condensed Sample-Guided Model Inversion for Knowledge Distillation
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design
Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Neural Network Compression for Reinforcement Learning Tasks
Torch2Chip: An End-to-end Customizable Deep Neural Network Compression and Deployment Toolkit for Prototype Hardware Accelerator Design
Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Towards Meta-Pruning via Optimal Transport
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning
Balanced and Deterministic Weight-sharing Helps Network Performance
ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation
Grokking as Compression: A Nonlinear Complexity Perspective
Causal-DFQ: Causality Guided Data-free Network Quantization
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Quantization Aware Factorization for Deep Neural Network Compression
Survey on Computer Vision Techniques for Internet-of-Things Devices
Model Compression Methods for YOLOv5: A Review
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.