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 3351–3375 of 4925 papers

TitleStatusHype
Verifying Quantized Neural Networks using SMT-Based Model Checking—0
Conditional COT-GAN for Video Prediction with Kernel SmoothingCode0
Harnessing Unrecognizable Faces for Improving Face Recognition—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Task-driven Semantic Coding via Reinforcement LearningCode1
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming—0
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution—0
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features—0
Granger Causality from Quantized Measurements—0
Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems—0
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers—0
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation—0
Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation—0
Transferable Sparse Adversarial AttackCode1
Weak target detection with multi-bit quantization in colocated MIMO radar—0
Integer-Only Neural Network Quantization Scheme Based on Shift-Batch-NormalizationCode0
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization—0
Linear-Time Self Attention with Codeword Histogram for Efficient RecommendationCode1
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systemsCode0
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization—0
Quantization and Deployment of Deep Neural Networks on MicrocontrollersCode0
HDRUNet: Single Image HDR Reconstruction with Denoising and DequantizationCode0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications—0
Post-Training Sparsity-Aware QuantizationCode1
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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