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 2026–2050 of 4925 papers

TitleStatusHype
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow AvoidanceCode0
Quantized distributed Nash equilibrium seeking under DoS attacks—0
Hybrid noise shaping for audio coding using perfectly overlapped window—0
Robust open-set classification for encrypted traffic fingerprintingCode0
Consistent Signal Reconstruction from Streaming Multivariate Time Series—0
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition—0
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond—0
Towards Clip-Free Quantized Super-Resolution Networks: How to Tame Representative Images—0
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Jumping through Local Minima: Quantization in the Loss Landscape of Vision TransformersCode1
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Dataset QuantizationCode2
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts—0
Quantization-based Optimization with Perspective of Quantum Mechanics—0
Analyzing Quantization in TVM—0
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations—0
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual LearningCode1
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
ResQ: Residual Quantization for Video Perception—0
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer—0
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs—0
Precision and Recall Reject Curves for Classification—0
Characteristics of networks generated by kernel growing neural gasCode0
Gradient-Based Post-Training Quantization: Challenging the Status Quo—0
A Survey on Model Compression for Large Language Models—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