SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3451–3500 of 4925 papers

TitleStatusHype
A Comprehensive Benchmark for Single Image Compression Artifacts Reduction—0
A comprehensive review of Binary Neural Network—0
A Comprehensive Study on Quantization Techniques for Large Language Models—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification—0
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge—0
A Compressed Sensing Approach for Distribution Matching—0
A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE—0
A Counterexample in Cross-Correlation Template Matching—0
Acoustic Model Compression with MAP adaptation—0
ACQ: Improving Generative Data-free Quantization Via Attention Correction—0
ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison—0
Activation Map-based Vector Quantization for 360-degree Image Semantic Communication—0
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training—0
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform—0
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation—0
Adaptive Asymmetric Label-guided Hashing for Multimedia Search—0
Adaptive Block Floating-Point for Analog Deep Learning Hardware—0
Adaptive Compression for Communication-Efficient Distributed Training—0
Adaptive Dataset Quantization—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image—0
Adaptive Dither Voting for Robust Spatial Verification—0
Adaptive Integrate-and-Fire Time Encoding Machine with Quantization—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning—0
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers—0
Adaptive Proximal Gradient Methods for Structured Neural Networks—0
Adaptive Quantization for Deep Neural Network—0
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks—0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning—0
Adaptive Quantization of Neural Networks—0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks—0
Adaptive quantization with mixed-precision based on low-cost proxy—0
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things—0
Adaptive Resource Allocation for Semantic Communication Networks—0
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression—0
Adaptive Training of Random Mapping for Data Quantization—0
Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks—0
Adaptive Wireless Image Semantic Transmission and Over-The-Air Testing—0
Adaptive Wireless Image Semantic Transmission: Design, Simulation, and Prototype Validation—0
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference—0
AdaQAT: Adaptive Bit-Width Quantization-Aware Training—0
A Data and Compute Efficient Design for Limited-Resources Deep Learning—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Additive Quantization for Extreme Vector Compression—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified