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

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Showing 4351–4400 of 4925 papers

TitleStatusHype
Prototype-based classifiers in the presence of concept drift: A modelling framework—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
Deep Log-Likelihood Ratio QuantizationCode0
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
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
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Low-Resolution Quantization in Phase Modulated Systems: Optimum Detectors and Error Rate Analysis—0
Distributed Learning with Sublinear Communication—0
Cluster Regularized Quantization for Deep Networks Compression—0
Real-Time detection, classification and DOA estimation of Unmanned Aerial Vehicle—0
Low-bit Quantization of Neural Networks for Efficient InferenceCode0
Novel Near-Optimal Scalar Quantizers with Exponential Decay Rate and Global Convergence—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
Binarized Knowledge Graph EmbeddingsCode0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
Same, Same But Different - Recovering Neural Network Quantization Error Through Weight FactorizationCode0
Collaborative Quantization for Cross-Modal Similarity Search—0
Supervised Quantization for Similarity Search—0
Deep Triplet QuantizationCode0
Robustness of Generalized Learning Vector Quantization Models against Adversarial AttacksCode0
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge—0
Model-Based Detector for SSDs in the Presence of Inter-cell Interference—0
Improving Neural Network Quantization without Retraining using Outlier Channel SplittingCode0
Distributed Learning with Compressed Gradient Differences—0
Subspace Robust Wasserstein Distances—0
Learning Space Partitions for Nearest Neighbor SearchCode0
Nonparametric Inference under B-bits Quantization—0
QGAN: Quantized Generative Adversarial Networks—0
Toward Joint Image Generation and Compression using Generative Adversarial Networks—0
On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments—0
Hybrid coarse-fine classification for head pose estimationCode0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison—0
Quantized Epoch-SGD for Communication-Efficient Distributed Learning—0
Mean Estimation from One-Bit Measurements—0
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images—0
DSConv: Efficient Convolution OperatorCode0
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks—0
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-AirCode0
Vector and Line Quantization for Billion-scale Similarity Search on GPUsCode0
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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