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 2651–2675 of 4925 papers

TitleStatusHype
Revisiting DNN Training for Intermittently-Powered Energy-Harvesting Micro-Computers—0
Revisiting Locality-Sensitive Binary Codes from Random Fourier Features—0
Revisiting Quantization Error in Face Alignment—0
Revisiting Uncertainty Estimation and Calibration of Large Language Models—0
Revolutionizing Mobile Interaction: Enabling a 3 Billion Parameter GPT LLM on Mobile—0
REx: Data-Free Residual Quantization Error Expansion—0
RFI Mitigation for One-bit UWB Radar Systems—0
Riemannian Manifold Embeddings for Straight-Through Estimator—0
RIS-Assisted Energy Harvesting Gains for Bistatic Backscatter Networks: Performance Analysis and RIS Phase Optimization—0
RIS-Assisted Self-Interference Mitigation for In-Band Full-Duplex Transceivers—0
Risk Assessment for Connected Vehicles under Stealthy Attacks on Vehicle-to-Vehicle Networks—0
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models—0
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions—0
RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction—0
Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image—0
Robust building footprint extraction from big multi-sensor data using deep competition network—0
Robust Dequantized Compressive Sensing—0
Robust Downlink Transmit Optimization under Quantized Channel Feedback via the Strong Duality for QCQP—0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems—0
Robust identification of thermal models for in-production High-Performance-Computing clusters with machine learning-based data selection—0
Restricted Minimum Error Entropy Criterion for Robust Classification—0
Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels—0
RobustMQ: Benchmarking Robustness of Quantized Models—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