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 2801–2825 of 4925 papers

TitleStatusHype
Variable-rate hierarchical CPC leads to acoustic unit discovery in speechCode1
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale TransformersCode2
Extreme Compression for Pre-trained Transformers Made Simple and Efficient—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
Long Scale Error Control in Low Light Image and Video Enhancement Using Equivariance—0
NIPQ: Noise proxy-based Integrated Pseudo-QuantizationCode1
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional MatchingCode1
On the Role of Spatial Effects in Early Estimates of Disease Infectiousness: A Second Quantization Approach—0
AMED: Automatic Mixed-Precision Quantization for Edge DevicesCode0
Re-parameterizing Your Optimizers rather than ArchitecturesCode2
Q-LIC: Quantizing Learned Image Compression with Channel Splitting—0
Efficient-Adam: Communication-Efficient Distributed Adam—0
QUIC-FL: Quick Unbiased Compression for Federated Learning—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Federated Split BERT for Heterogeneous Text Classification—0
Sparse*BERT: Sparse Models Generalize To New tasks and Domains—0
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps—0
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models—0
Wavelet Feature Maps Compression for Image-to-Image CNNsCode1
Approximation speed of quantized vs. unquantized ReLU neural networks and beyond—0
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language UnderstandingCode1
Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment—0
OPQ: Compressing Deep Neural Networks with One-shot Pruning-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