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 451–475 of 4925 papers

TitleStatusHype
Feature Quantization Improves GAN TrainingCode1
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
DVD-Quant: Data-free Video Diffusion Transformers QuantizationCode1
LaCo: Large Language Model Pruning via Layer CollapseCode1
Dynamic Network Quantization for Efficient Video InferenceCode1
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D DetectionCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Learned Step Size QuantizationCode1
Learning Cross-Scale Weighted Prediction for Efficient Neural Video CompressionCode1
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense RetrievalCode1
Learning Graph Quantized TokenizersCode1
Learning Statistical Texture for Semantic SegmentationCode1
Animation from Blur: Multi-modal Blur Decomposition with Motion GuidanceCode1
EasyQuant: Post-training Quantization via Scale OptimizationCode1
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense RetrievalCode1
Adapting LLaMA Decoder to Vision TransformerCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
A Statistical Framework for Low-bitwidth Training of Deep Neural NetworksCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
LiMuSE: Lightweight Multi-modal Speaker ExtractionCode1
Catastrophic Failure of LLM Unlearning via QuantizationCode1
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital FossaCode1
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
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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