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 3451–3475 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
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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