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 1751–1800 of 4925 papers

TitleStatusHype
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference—0
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
Feature Quantization for Defending Against Distortion of Images—0
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection—0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
BitTTS: Highly Compact Text-to-Speech Using 1.58-bit Quantization and Weight Indexing—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
Federated Learning in Adversarial Settings—0
Federated Learning: Strategies for Improving Communication Efficiency—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—0
Federated Learning With Quantized Global Model Updates—0
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization—0
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference—0
Federated Split BERT for Heterogeneous Text Classification—0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM—0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization—0
Dual Codebook VQ: Enhanced Image Reconstruction with Reduced Codebook Size—0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization—0
A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: the Random Phase Case—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning—0
Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization—0
A Blockchain Solution for Collaborative Machine Learning over IoT—0
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications—0
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment—0
FewGAN: Generating from the Joint Distribution of a Few Images—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model—0
FFN Fusion: Rethinking Sequential Computation in Large Language Models—0
DSConv: Efficient Convolution Operator—0
A New Old Idea: Beam-Steering Reflectarrays for Efficient Sub-THz Multiuser MIMO—0
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform—0
Fighting Quantization Bias With Bias—0
Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks—0
Fully Quantized Network for Object Detection—0
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition—0
Fully Quantized Transformer for Machine Translation—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING—0
Show:102550
← PrevPage 36 of 99Next →
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