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 3751–3775 of 4925 papers

TitleStatusHype
Automatic Gain Control Design for Dynamic Visible Light Communication Systems—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs—0
Automatic Mixed-Precision Quantization Search of BERT—0
Automatic Network Adaptation for Ultra-Low Uniform-Precision Quantization—0
Automatic Parameter Tying in Neural Networks—0
Automatic Pruning for Quantized Neural Networks—0
Automating Nearest Neighbor Search Configuration with Constrained Optimization—0
AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning—0
Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion—0
AutoQ: Automated Kernel-Wise Neural Network Quantization—0
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks—0
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications—0
Auto-regressive Image Synthesis with Integrated Quantization—0
Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations—0
Autoregressive Speech Synthesis without Vector Quantization—0
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge—0
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization—0
Avaliação do método dialético na quantização de imagens multiespectrais—0
A Video Coding Method Based on Neural Network for CLIC2024—0
A Vision System for Multi-View Face Recognition—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models—0
A White Paper on Neural Network Quantization—0
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent—0
Show:102550
← PrevPage 151 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