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 4326–4350 of 4925 papers

TitleStatusHype
Training Quantized Neural Networks with a Full-precision Auxiliary Module—0
Robustness of Neural Networks to Parameter Quantization—0
Local Orthogonal Decomposition for Maximum Inner Product Search—0
Variational Inference with Latent Space Quantization for Adversarial ResilienceCode0
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMMCode0
Deep Learning on Mobile Devices - A Review—0
Learning Convolutional Transforms for Lossy Point Cloud Geometry CompressionCode0
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural NetworksCode0
Prototype-based classifiers in the presence of concept drift: A modelling framework—0
Galaxy classification: A machine learning analysis of GAMA catalogue data—0
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image ClassificationCode0
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution—0
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
Deep Log-Likelihood Ratio QuantizationCode0
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)Code0
Focused Quantization for Sparse CNNsCode0
On the Quantization of Cellular Neural Networks for Cyber-Physical Systems—0
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications—0
Learning low-precision neural networks without Straight-Through Estimator(STE)—0
TamperNN: Efficient Tampering Detection of Deployed Neural Nets—0
Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Distributed Learning with Sublinear Communication—0
Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle—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