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 3951–4000 of 4925 papers

TitleStatusHype
QGAN: Quantized Generative Adversarial Networks—0
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits—0
QGen: On the Ability to Generalize in Quantization Aware Training—0
QKD: Quantization-aware Knowledge Distillation—0
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity—0
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments—0
Q-LIC: Quantizing Learned Image Compression with Channel Splitting—0
QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation—0
Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration—0
QMamba: Post-Training Quantization for Vision State Space Models—0
Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network—0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution—0
QNNRepair: Quantized Neural Network Repair—0
QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking—0
Q-PETR: Quant-aware Position Embedding Transformation for Multi-View 3D Object Detection—0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
QPruner: Probabilistic Decision Quantization for Structured Pruning in Large Language Models—0
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization—0
Qrazor: Reliable and effortless 4-bit llm quantization by significant data razoring—0
QReg: On Regularization Effects of Quantization—0
QRMODA and BRMODA: Novel Models for Face Recognition Accuracy in Computer Vision Systems with Adapted Video Streams—0
Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization—0
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models—0
Q-SAM2: Accurate Quantization for Segment Anything Model 2—0
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
QSID-MPC: Model Predictive Control with System Identification from Quantized Data—0
Q-SNNs: Quantized Spiking Neural Networks—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
QSpec: Speculative Decoding with Complementary Quantization Schemes—0
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks—0
QSViT: A Methodology for Quantizing Spiking Vision Transformers—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Quadapter: Adapter for GPT-2 Quantization—0
QuAILoRA: Quantization-Aware Initialization for LoRA—0
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM—0
Qualitative detection of oil adulteration with machine learning approaches—0
Quality Adaptive Low-Rank Based JPEG Decoding with Applications—0
Quality Scalable Quantization Methodology for Deep Learning on Edge—0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning—0
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks—0
QuantEase: Optimization-based Quantization for Language Models—0
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex—0
Quantifying the Capabilities of LLMs across Scale and Precision—0
Quantifying the Capacity Gains in Coarsely Quantized SISO Systems with Nonlinear Analog Operators—0
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction—0
Quantisation-aware Precoding for MU-MIMO with Limited-capacity Fronthaul—0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs—0
Quantity over Quality: Dithered Quantization for Compressive Radar Systems—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