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 3951–3975 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
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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