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 2601–2625 of 4925 papers

TitleStatusHype
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation—0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting—0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—0
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks—0
Multi-Sample Training for Neural Image Compression—0
The Cramer-Rao Bound for Signal Parameter Estimation from Quantized Data—0
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language ModelsCode1
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile—0
Device-friendly Guava fruit and leaf disease detection using deep learningCode0
SpeedLimit: Neural Architecture Search for Quantized Transformer Models—0
Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ)—0
Vector Quantized Semantic Communication System—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks—0
Flexible Neural Image Compression via Code Editing—0
SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision—0
MoVQ: Modulating Quantized Vectors for High-Fidelity Image GenerationCode5
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems—0
Quantization for decentralized learning under subspace constraints—0
Analysis of Quantization on MLP-based Vision Models—0
Compressed Particle-Based Federated Bayesian Learning and Unlearning—0
Efficient Quantized Sparse Matrix Operations on Tensor CoresCode1
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds—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