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 3726–3750 of 4925 papers

TitleStatusHype
Attention-aware Post-training Quantization without Backpropagation—0
Attention based on-device streaming speech recognition with large speech corpus—0
Attention-based Transducer for Online Speech Recognition—0
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency—0
Attention Round for Post-Training Quantization—0
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking—0
Attribute Artifacts Removal for Geometry-based Point Cloud Compression—0
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization—0
A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation—0
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM—0
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent—0
A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Models—0
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks—0
Autoencoder-Based Error Correction Coding for One-Bit Quantization—0
Autoencoder based image compression: can the learning be quantization independent?—0
Automated Backend-Aware Post-Training Quantization—0
Automated design of error-resilient and hardware-efficient deep neural networks—0
Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA—0
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline—0
Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images—0
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks—0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—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