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 1776–1800 of 4925 papers

TitleStatusHype
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 72 of 197Next →
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