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 3651–3675 of 4925 papers

TitleStatusHype
FEDZIP: A Compression Framework for Communication-Efficient Federated LearningCode0
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering—0
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices—0
Rescuing Deep Hashing from Dead Bits Problem—0
Understanding Cache Boundness of ML Operators on ARM ProcessorsCode0
CAMBI: Contrast-aware Multiscale Banding Index—0
Performance of Cell-Free MmWave Massive MIMO Systems with Fronthaul Compression and DAC Quantization—0
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence—0
Pruning and Quantization for Deep Neural Network Acceleration: A Survey—0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Overfitting for Fun and Profit: Instance-Adaptive Data Compression—0
Generative Zero-shot Network Quantization—0
Time-Correlated Sparsification for Communication-Efficient Federated Learning—0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization—0
On the quantization of recurrent neural networks—0
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Computational data analysis for first quantization estimation on JPEG double compressed imagesCode0
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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