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 2751–2775 of 4925 papers

TitleStatusHype
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection—0
Self-Adaptable Templates for Feature Coding—0
Self-calibration for Language Model Quantization and Pruning—0
Self-control: A Better Conditional Mechanism for Masked Autoregressive Model—0
Self-Distilled Quantization: Achieving High Compression Rates in Transformer-Based Language Models—0
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval—0
Self-triggered Consensus of Multi-agent Systems with Quantized Relative State Measurements—0
Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression—0
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation—0
Semantic Residual for Multimodal Unified Discrete Representation—0
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
Semantic Text Compression for Classification—0
Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence—0
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization—0
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization—0
Semi-supervised Vector-Quantization in Visual SLAM using HGCN—0
Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware—0
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation—0
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions—0
Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC—0
SEP-Nets: Small and Effective Pattern Networks—0
SeRP: Self-Supervised Representation Learning Using Perturbed Point Clouds—0
Service Delay Minimization for Federated Learning over Mobile Devices—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