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 1751–1775 of 4925 papers

TitleStatusHype
Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding—0
Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
Approximately Invertible Neural Network for Learned Image Compression—0
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers—0
Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance AnalysisCode0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
On-device AI: Quantization-aware Training of Transformers in Time-Series—0
The Uniqueness of LLaMA3-70B Series with Per-Channel Quantization—0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things—0
Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO—0
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation—0
On-Device Language Models: A Comprehensive ReviewCode0
Infrared Domain Adaptation with Zero-Shot Quantization—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
Revisiting DNN Training for Intermittently-Powered Energy-Harvesting Micro-Computers—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
DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding—0
Matmul or No Matmal in the Era of 1-bit LLMs—0
Disentangling segmental and prosodic factors to non-native speech comprehensibility—0
Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing—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