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 3501–3525 of 4925 papers

TitleStatusHype
A White Paper on Neural Network Quantization—0
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier IntegralsCode0
Development of Quantized DNN Library for Exact Hardware Emulation—0
Compositional Sketch SearchCode0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization—0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates—0
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback—0
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning—0
Conditional COT-GAN for Video Prediction with Kernel SmoothingCode0
Verifying Quantized Neural Networks using SMT-Based Model Checking—0
Cross-Modal Discrete Representation Learning—0
SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network—0
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks—0
Harnessing Unrecognizable Faces for Improving Face Recognition—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming—0
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features—0
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution—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
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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