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 2476–2500 of 4925 papers

TitleStatusHype
Quantized distributed Nash equilibrium seeking under DoS attacks—0
Hybrid noise shaping for audio coding using perfectly overlapped window—0
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition—0
Consistent Signal Reconstruction from Streaming Multivariate Time Series—0
Robust open-set classification for encrypted traffic fingerprintingCode0
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
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts—0
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Quantization-based Optimization with Perspective of Quantum Mechanics—0
Analyzing Quantization in TVM—0
ResQ: Residual Quantization for Video Perception—0
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations—0
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer—0
Precision and Recall Reject Curves for Classification—0
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs—0
Characteristics of networks generated by kernel growing neural gasCode0
AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model—0
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear MappingCode0
A Survey on Model Compression for Large Language Models—0
Gradient-Based Post-Training Quantization: Challenging the Status Quo—0
Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning—0
Efficient Neural PDE-Solvers using Quantization Aware Training—0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
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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