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 4001–4025 of 4925 papers

TitleStatusHype
Quantization Algorithms for Random Fourier Features—0
Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding—0
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging—0
Quantization Aware Factorization for Deep Neural Network Compression—0
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control—0
Quantization-Aware Phase Retrieval—0
Quantization Aware Training, ERNIE and Kurtosis Regularizer: a short empirical study—0
Quantization Backdoors to Deep Learning Commercial Frameworks—0
Quantization-based Bermudan option pricing in the FX world—0
Quantization-based Bounds on the Wasserstein Metric—0
Quantization based Fast Inner Product Search—0
Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization—0
Quantization-based Optimization with Perspective of Quantum Mechanics—0
Quantization Design for Deep Learning-Based CSI Feedback—0
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization—0
Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training—0
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization—0
Quantization for decentralized learning under subspace constraints—0
Quantization for Rapid Deployment of Deep Neural Networks—0
Quantization goes Polynomial—0
Quantization-Guided Training for Compact TinyML Models—0
Quantization in Layer's Input is Matter—0
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation—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