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 2926–2950 of 4925 papers

TitleStatusHype
Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC—0
SEP-Nets: Small and Effective Pattern Networks—0
SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds—0
Service Delay Minimization for Federated Learning over Mobile Devices—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Serving Large Language Models on Huawei CloudMatrix384—0
Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems—0
SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic—0
SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook—0
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization—0
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations—0
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions—0
Shared Predictive Cross-Modal Deep Quantization—0
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks—0
Shining light on data: Geometric data analysis through quantum dynamics—0
Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search—0
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features—0
SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network—0
Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study—0
MERCURY: Accelerating DNN Training By Exploiting Input Similarity—0
Simple and Effective Unsupervised Redundancy Elimination to Compress Dense Vectors for Passage Retrieval—0
Neural Speech Coding for Real-time Communications using Constant Bitrate Scalar Quantization—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Simple strategies for recovering inner products from coarsely quantized random projections—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