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 1276–1300 of 4925 papers

TitleStatusHype
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated LearningCode0
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language ModelsCode0
Learning Accurate Low-Bit Deep Neural Networks with Stochastic QuantizationCode0
Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions RecognitionCode0
HOT: Hadamard-based Optimized TrainingCode0
Device-friendly Guava fruit and leaf disease detection using deep learningCode0
Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge DevicesCode0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
Homology-constrained vector quantization entropy regularizerCode0
A multimodal dynamical variational autoencoder for audiovisual speech representation learningCode0
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniquesCode0
High-Accuracy Low-Precision TrainingCode0
BdSLW60: A Word-Level Bangla Sign Language DatasetCode0
Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUsCode0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise ReductionCode0
Hierarchical Quantized Representations for Script GenerationCode0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependenceCode0
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization PerformanceCode0
HDRUNet: Single Image HDR Reconstruction with Denoising and DequantizationCode0
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural NetworksCode0
Hessian Aware Quantization of Spiking Neural NetworksCode0
Harnessing Large Language Models Locally: Empirical Results and Implications for AI PCCode0
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone NetworksCode0
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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