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 3626–3650 of 4925 papers

TitleStatusHype
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—0
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs—0
Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint—0
Quantized Neural Networks for Radar Interference Mitigation—0
Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression—0
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning—0
Evaluation of quality measures for color quantization—0
A Review of Recent Advances of Binary Neural Networks for Edge Computing—0
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet VocoderCode0
HAWQV3: Dyadic Neural Network QuantizationCode1
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing—0
Ultra-Lightweight Speech Separation via Group Communication—0
Layer-Wise Data-Free CNN Compression—0
Visual Time Series Forecasting: An Image-driven Approach—0
Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks—0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements—0
Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data—0
PAMS: Quantized Super-Resolution via Parameterized Max ScaleCode1
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns—0
Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference—0
Subtensor Quantization for Mobilenets—0
Paralinguistic Privacy Protection at the Edge—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
Robust building footprint extraction from big multi-sensor data using deep competition network—0
Show:102550
← PrevPage 146 of 197Next →
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