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 4651–4675 of 4925 papers

TitleStatusHype
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
Variational quantization for state space modelsCode0
Weakly Supervised Deep Hyperspherical Quantization for Image RetrievalCode0
TernaryBERT: Distillation-aware Ultra-low Bit BERTCode0
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product QuantizationCode0
Forward and Backward Information Retention for Accurate Binary Neural NetworksCode0
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural NetworksCode0
Characteristics of networks generated by kernel growing neural gasCode0
Recurrent Neural Networks With Limited Numerical PrecisionCode0
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning ClassificationCode0
An asymmetric heuristic for trained ternary quantization based on the statistics of the weights: an application to medical signal classificationCode0
Partition Map-Based Fast Block Partitioning for VVC Inter CodingCode0
RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNsCode0
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language ModelsCode0
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence EmbeddingsCode0
Integrated Encoding and Quantization to Enhance Quanvolutional Neural NetworksCode0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed VideoCode0
Trainable pruned ternary quantization for medical signal classification modelsCode0
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural RecordingCode0
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman CodingCode0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Integral Human Pose RegressionCode0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge IntelligenceCode0
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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