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 2626–2650 of 4925 papers

TitleStatusHype
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol—0
Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data—0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs—0
Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation—0
Resource-Efficient Language Models: Quantization for Fast and Accessible Inference—0
Resource-Efficient Neural Networks for Embedded Systems—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Resource-Efficient Transformer Architecture: Optimizing Memory and Execution Time for Real-Time Applications—0
ResQ: Residual Quantization for Video Perception—0
Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules—0
Résumé abstractif à partir d'une transcription audio—0
Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing—0
Rethinking Diffusion for Text-Driven Human Motion Generation—0
Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression—0
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis—0
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View—0
Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint—0
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning—0
Rethinking Neural Network Quantization—0
Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach—0
Retraining-Based Iterative Weight Quantization for Deep Neural Networks—0
Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC—0
Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking—0
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—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