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 1026–1050 of 4925 papers

TitleStatusHype
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical ImagesCode0
Merge-Friendly Post-Training Quantization for Multi-Target Domain AdaptationCode0
Communication Efficient Private Federated Learning Using DitheringCode0
Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and ImplementationCode0
Loss Aware Post-training QuantizationCode0
Communication-Efficient Multi-Device Inference Acceleration for Transformer ModelsCode0
Communication-Efficient Federated Learning via Predictive CodingCode0
Loss-aware Weight Quantization of Deep NetworksCode0
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and QuantizationCode0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
Additive Noise Annealing and Approximation Properties of Quantized Neural NetworksCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and VulnerabilitiesCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Communication-Efficient Distributed Blockwise Momentum SGD with Error-FeedbackCode0
LISA: Learning Interpretable Skill Abstractions from LanguageCode0
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific SensingCode0
Linearly Converging Error Compensated SGDCode0
Accelerating PoT Quantization on Edge DevicesCode0
Communication-Censored Distributed Stochastic Gradient DescentCode0
Lightweight Client-Side Chinese/Japanese Morphological Analyzer Based on Online LearningCode0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO SystemsCode0
Light Multi-segment Activation for Model CompressionCode0
Show:102550
← PrevPage 42 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