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 1051–1075 of 4925 papers

TitleStatusHype
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers—0
Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language ModelCode3
Approximately Invertible Neural Network for Learned Image Compression—0
Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance AnalysisCode0
On-device AI: Quantization-aware Training of Transformers in Time-Series—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
The Uniqueness of LLaMA3-70B Series with Per-Channel Quantization—0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
On-Device Language Models: A Comprehensive ReviewCode0
Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO—0
Training-Free Activation Sparsity in Large Language ModelsCode2
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things—0
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bitCode1
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
Quantized neural network for complex hologram generation—0
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized ModelsCode0
Variational autoencoder-based neural network model compression—0
MobileQuant: Mobile-friendly Quantization for On-device Language ModelsCode2
Revisiting DNN Training for Intermittently-Powered Energy-Harvesting Micro-Computers—0
Infrared Domain Adaptation with Zero-Shot Quantization—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Informational Embodiment: Computational role of information structure in codes and robots—0
Quantization-aware Matrix Factorization for Low Bit Rate Image CompressionCode1
DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding—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